Ning Ge

Shin Nakajima

Marc Pantel

Efficient Online Analysis of Accidental Fault Localization for Dynamic Systems using Hidden Markov Model

Keywords: Simulation, Online Analysis, Accidental Fault Localization, Hidden Markov Model

This paper proposes a novel approach to do online analysis of accidental fault localization for dynamic systems by using Hidden Markov Model (HMM). By introducing reasonable and appropriate abstraction of complex system, HMM is used to represent the fault and no-fault states of system's components and system's behaviour. The HMM is parametrized to be statistically equivalent to real system's behaviour. Inspired by the principles of Fault Tree Analysis and maximum entropy in Bayesian probability theory, we propose the algorithms to estimate HMM's parameters, instead of learning, because in real systems the learning data for accidental fault is difficult to obtain. We design a specific test bed to generate large quantity of test cases, and give out the experimental results to assess the accuracy and efficiency. Meanwhile, we apply the approach to a simple helicopter control system case study, and give out convincing results.

INTRODUCTION

Fault Detection and Isolation (FDI) is dedicated to monitoring a system, identifying when a fault has occurred, and pinpointing the type of fault and its location. One main FDI approach derives the faults from some model, classified into the category Model-Based FDI, while another main category is Signal Processing based FDI. In model-based FDI, the system model may be mathematical, or knowledge based, including observer-based approach, parity-space approach, parameter identification based methods, etc [START_REF] Ding | Basic ideas, major issues and tools in the observer-based FDI framework[END_REF]. As the dynamic systems' complexity increases, the resource-consuming simulation technology is insufficient for detecting faults in systems. Thus, it becomes urgent to use abstract model to represent complex system by keeping necessary and sufficient information. The efficiency and accuracy of model-based approach depends on the appropriate abstraction and reasonable assumptions.

Many theories and techniques exist for the analysis and simulation of large dynamic systems. Our previous work [START_REF] Nakajima | Co-analysis of sysml and simulink models for cyber-physical systems design[END_REF] proposes a co-analysis framework for the automated analysis of cyber-physical systems (CPS) [START_REF] Wing | [END_REF], in which the behaviour of Simulink is taken in an as-is manner determined by the simulation algorithms. The approach combines logic-based formal analysis methods with numerical simulations to enable the analysis of an under-constrained controller design, which cannot be handled by co-simulation. An open question proposed by the perspective in [START_REF] Nakajima | Co-analysis of sysml and simulink models for cyber-physical systems design[END_REF] concerns the fault detection and localization in systems. CPS introduce a new paradigm to software-intensive systems, in which the controller (system) is strongly affected by feedback from the plant (environment). As shown in Fig. 1, a controller may have several control modes and changes itself with transitions between the modes. Since some of the Simulink model descriptions represent hardware components, the controller is expected to be able to do online analysis of the accidental fault localization F (t) occurring in the hardware. In this paper, we aim to solve the above problem by using a statistical model, Hidden Markov Model (HMM) [START_REF] Rabiner | A tutorial on hidden markov models and selected applications in speech recognition[END_REF]. In HMM, the system being modeled is assumed to be a Markov process with unobserved (hidden) states. By analysing the observed outputs of the hidden states, system's behaviour can be deduced. HMM has thus been widely used in temporal pattern recognition such as speech, handwriting, gesture recognition, etc. HMM has been used to diagnose and prognose the whole system's health condition in some existing works [START_REF] Bjerkeseth | Using hidden markov models for fault diagnostics and prognostics in condition based maintenance systems[END_REF] [START_REF] Tobon-Mejia | A data-driven failure prognostics method based on mixture of gaussians hidden markov models[END_REF]. The learning tasks are used to train the parameters in HMM. However, the learning task is based on rich observed sequences with enough fault experience, which is not easy to obtain for accidental faults. In this work, we propose a novel approach to use HMM to do online analysis of accidental fault localization in dynamic systems. We introduce appropriate abstraction of complex dynamic systems and reasonable assumptions of HMM parameters, to represent the fault and no-fault states of system's components and system's behaviour. Inspired by the principles of Fault Tree Analysis (FTA) [START_REF] Vesely | Fault Tree Handbook[END_REF] and of maximum entropy [START_REF] Jaynes | Information Theory and Statistical Mechanics -Jaynes[END_REF] in Bayesian probabil-ity theory, we propose an algorithm to estimate the parameters in HMM without learning task. We evaluate the accuracy and efficiency of proposed algorithm by using a specific test bed to generate 1000 test cases, and then apply the approach to a simple helicopter control system case study.

In paper [START_REF] Clarke | Statistical model checking for cyber-physical systems[END_REF], the author aims to prove the validation of CPS's behavioural properties by statistical model. Our main ideas are identical: using statistical model to deduce the conclusion, except that [START_REF] Clarke | Statistical model checking for cyber-physical systems[END_REF] introduces a formal description in BLTL (Bounded Linear Temporal Logic), instead of HMM, to interpret the probable result sequence, which is the same concept as our observation sequence. By using importancesampling and cross-entropy (two classic statistical methods), the main obstacle [START_REF] Clarke | Statistical model checking for cyber-physical systems[END_REF] encounters is that it needs much more samples to give out a convincing conclusion. The reason behind is that BLTL treats each element in a result sequence in a pure statistical thus relatively independent way, while HMM assumes that there must be some explicit dependency between the elements. This paper is organized as follows: Section 2. details the problem we aim to solve; Section 3. gives an overview of our approach by introducing HMM modeling and analysis methods; Section 4. presents the approach of online analysing accidental fault localization for complex dynamic systems by using HMM; Section 5. experiments the approach in a specific test bed; applies the approach to a simple helicopter control system case study; and discusses the generalization of the approach; Section 6. gives some concluding and perspective remarks.

PROBLEM DESCRIPTION

The accidental fault may occur on the hardware during system's execution. Since the accidental fault does not frequently occurs, a reasonable assumption is made in this work. Assumption 1. A system encounters accidental fault means at this moment, at most one device is the fault source. Two devices cannot encounter accidental fault at the same time.

The prior knowledge in this work consists of devices' default parameters and system's specification. The device's default parameters indicate a device's fault occurrence probability. This parameter is usually provided by the device manufacture according to the testing result before selling. Alternatively, MTBF (Mean Time Between Failures) can be used to deduce this parameter if information is lacking. System's specification describes the expected functional constraints.

Example 1 (Problem Description Example). During a finite simulation period T , we choose N observation time points for the system with components A, B, C, D, etc (Fig. 2). Assume M functional constraints (FC) are described in the specification. v i (i = 1...11) are output/input variables between the components.

! " # $ ! ! ! " ! # ! $ ! % ! & ! ' ! ( ! ) ! !* ! !! Figure 2. Problem Description Example
After time T , we obtain the violation status (R for respecting specification, V for violating specification) for the M functional constraints at time points t (t = 1...N), i.e.

FC t 1 (v 1 , v 3 , v 5 ) = V ... FC t M (v 2 ) = R
When a functional constraint is violated, one of the devices must have encountered an accidental fault. However once we detect a wrong output from a device, usually the others' outputs will be bad due to failure propagation. Thus the problem is distinguishing and locating accidental fault's source device. This can be considered as a pattern recognition question. Moreover, the accidental fault localization approach should support online analysis, which implies that the method needs to be computation-economic and easy to implemented by most onboard dynamic systems.

The simulation technology is able to provide analysable evidence to help fault identification. Nevertheless, it lacks the mechanism to automatically locate the accidental fault. We introduce HMM in this work, because HMM, a good compromise between Bayesian network and time-based sequence, disposes of a natural capacity to deal with time-related pattern recognition problems. As shown in Fig. 3, HMM can be used to diagnostics, prognostics and online analysis. On the other hand, HMM, as the abstraction of real system, simulates statistically system's inner states and their behaviours. Once HMM is trained or built, it can be used to diagnose system's past and current health condition and prognoses future condition. Some works are aimed to detect the whole system's health condition, while in this work we aim to go further, to locate the fault source in the system.

HMM MODELING AND ANALYSIS

An HMM is defined as a statistical model used to represent stochastic processes, where the states are not directly observed. A basic HMM can be described as follows: HMM, as abstract model of real system, is statistically identical to system's real behaviour. When modeling a system, HMM separates the concept into two conceptually independent paradigms: behaviour and observation. Behaviour refers to what the system really is; while observation to what the system exhibits that is used for its recognition. M I gives indication about the probability that a behaviour becomes the first behaviour when system runs. M T decides how probably will the system behave from one state to the other states. This is statistically equivalent to the real system's behaviour. M E provides a distribution that connects the behaviour and the observation: if at a given time the behaviour is known, how probably an observed sequence will occur.

M I , M T and M E can be obtained by modelling or through a learning process. Once all these matrix parameters are estimated, HMM is capable to deduce, given an observed output sequence or a set of such sequences, the maximum likelihood estimation of inner-state transition sequences. The work [START_REF] Bjerkeseth | Using hidden markov models for fault diagnostics and prognostics in condition based maintenance systems[END_REF] shows a traditional way of applying HMM to system diagnosis and prognosis, as shown in Fig. 5. The trained HMM will

!"##$%&'()*+,& '+#$- ./01$2 340$),$#& .$56$%7$0 8)+4(4"-"1/& 8()(2$1$)0 &340$),$ &9$()%"%: ;01"2(1$ ./01$2 <6))$%1&<+%#"1"+% <(-76-(1$ =616)$&340$),$#& .$56$%7$0& 8)+4(4"-"1/ 8)$#"71 ./01$2 =616)$&<+%#"1"+% Figure 5.
Traditional HMM Approach be used to estimate system's past condition, predict system's future condition and compute a future observed sequence's occurrence probability.

In Ex. 2, we can observe an output sequence Seq(t) from the observers Output R and Output V within a certain observation time T , e.g.

V V V V R V V V .
As the parameters are pre-defined, we can derive system's behaviour State(t) during T by using Viterbi algorithm [START_REF] Viterbi | Error bounds for convolutional codes and an asymptotically optimum decoding algorithm[END_REF], in which H means healthy, and F means faulty, i.e.

F F F F H F F F .

In this work, our objective is to find out how system performs in terms of behaviour, however, the only available information source for the external world is the observation. Therefore a backward analysis is necessary to recover the behaviour from the observation. As we aim to tell the exact location of the fault, not only detecting unhealthy condition of a system, the core issue is how to construct HMM representing a system with probable fault.

The learning task cannot be used in this work. Because the accidental fault does not frequently happen, thus the learning task cannot be trained with rich fault experience. The modeling method in our research allows to pre-define some HMM parameters by using prior knowledge of devices' fault occurrence probability.

ONLINE ANALYSIS OF ACCIDENTAL FAULT LOCALIZATION

The approach takes all the components in a system as a whole model. When modeling HMM, a hidden state represents, at this moment, on which components one accidental fault occurs, or not. Instead of using learning task to train the parameters in HMM, we define the specific states and observations dedicated to accidental fault localization, and propose our own algorithms to compute the HMM's parameters by analysing system's architecture and using devices' fault occurrence probability. The proposed algorithms have been evaluated in the experiments by using our test bed and real case study, which will be presented in the next part.

Definition 1 (Physical Variable). A component C in a system has N I inputs and N O outputs variables, which connect C with other components. These N I together with N O variables are the physical variables of C.

By running the system, for some physical variables, whether it violates or not the functional constraints will be known. If it is possible to observe M physical variables' value sequence, the whole system can be modeled by M HMMs. Each represents whether current system's behaviour will lead to one physical variable violating the functional constraints, and which kind of violation.

System States

System states represent the health condition of all its components. A system with N components is seen as a coupling entity with N + 1 states, each of them represents at the given moment, which component encounters a certain context that leads it to give out unwanted output. HMM states are either all healthy (N F ) or having one faulty component (F i ), modeled as

N F F 1 F 2 ... F i ... F N .

Observations & Observed Sequence

The observations represent that physical variables violate the functional constraints or not.

Definition 2 (Non-Violation (V N )). When a physical variable does not violate functional constraint, this is defined as non-violation.

Definition 3 (Independent Violation (V I )). When a physical variable v violates functional constraint with only this variable, i.e. FC(v), this is defined as independent violation.

Definition 4 (Coupling Violation (V C )). When a physical variable v violates functional constraint with other variables, i.e. FC ′ (v, v 1 , v 2 , ...), the source of violation cannot be confirmed among the variables. The probability of each is thus given out. It is defined as coupling violation.

Each HMM has these 3 observations: V N , V I and V C . A system with M physical variables has M HMMs (Fig. 6).

!"#$ % & & % & ' ( % ( ! ( ) (*+ ' (*+ , (*+ # !!! !!! % & & % & ' ( % ( ! ( ) !!! !"#$ Figure 6. System's HMM Structure

Initial Probability Distribution M I

The devices' default occurrence parameters are provided by the manufacture. A component's fault parameter can be estimated when it is well designed, e.g. a functional block provided by the library of Simulink can be fully trusted, with fault occurrence parameter 0. For a system with N components, let ω i (i = 1...N) be the fault occurrence parameter of the i th component. M I is thus computed as follows:

M I (0) = N ∏ i=1 (1 -ω i ) N ∏ i=1 (1 -ω i ) + N ∑ i=1 ω i (1) M I (i) = ω i N ∏ i=1 (1 -ω i ) + N ∑ i=1 ω i , i = 1...N (2)
Eqs. 1 and 2 describe the likelihood that no component has failed at time 0 (i.e., no failure at the start) and the likelihood that some component i has failed at the start.

If prior knowledge about ω i is unknown, MTBF is used to deduce the initial parameters. Therefore, the most reasonable hypothesis is

N ∑ i, j=1
|ω i -ω j | = 0. If some indications exist, like the i th component is twice more probable to have fault than the j th , then ω i = 2ω j will replace ω i = ω j .

Transition Probability Matrix M T

M T (i, j) represents the probability of the transition from state i to state j. We propose to compute M T by using the devices' fault parameters ω i . When the system transits from current state to all components healthy state, the transition probability is

N ∏ k=1 (1 -ω k ).
When the system transits to one component faulty state j, the probability is

ω j N ∏ k=1,k = j (1-ω k ).
After normalization, M T is as follows:

M T (i, 0) = N ∏ k=1 (1 -ω k ) N ∏ k=1 (1 -ω k ) + N ∑ n=1 (ω n N ∏ k=1,k =n (1 -ω k )) , i = 0...N (3) M T (i, j) = ω j N ∏ k=1,k = j (1 -ω k ) N ∏ k=1 (1 -ω k ) + N ∑ n=1 (ω n N ∏ k=1,k =n (1 -ω k )) , i = 0...N, j = 1...N (4)
Eqs. 3 and 4 are the probability from component i failing to no component failing at a time t > 0 and from component i failing to component j failing at time t > 0.

Emission Probability Matrix M E

M E represents, if a component occurs fault, how probably it will influence the functional constraints. More precisely, how probably the physical variables will violate the functional constraints. Inspired by the principles of FTA and the principle of maximum entropy in Bayesian probability theory, we propose the algorithm for computing M E in Fig. 7. We will explain the definitions and the proposed algorithms in the following parts.

Definition 6 (Influence Graph G I ). Influence graph is a graph representing how components' accidental fault influences physical variables. Influence graph is topologically identical to dependency graph, with supplementary influence weight indicating the probability that the component C influences the variables v i . The influence weights G I (C, v i ) are computed by using Influence Layout Algorithm.

Example 3 (Influence Graph Example). Fig. 8 is an influence graph with 9 components and 15 physical variables. G I (A, v 3 ) is the influence weight between A and v 3 .

Algorithm 1 (Influence Layout Algorithm). Inspired by the principles of FTA, we propose this influence layout algorithm. An influence graph with N components and M variables can be solved by applying linear programming to Eq

. 5-8 . M ∑ i=1 v i = 1 (5) ! " # $ % & ' ( ) ! " ! # ! $ ! % ! & ! ' ! ( ! ) ! * ! "+ ! "" ! "# ! "$ ! "% ! "& , -./0! $ 1 Figure 8. Influence Graph Example v i > 1 δM , i = 1...M, δ > 1 (6)
For each component C, the sum of input value equals to the sum of its output value, to ensure C does not introduce supplementary side effect to system's failure analysis (Eq. 7).

∑ in v i -∑ out v j = 0 (7)
According to the principle of maximum entropy in Bayesian probability theory, without any prior knowledge, the difference between variables should be minimised (Eq. 8).

min v 1 ...v M M ∑ i=1 |v i - 1 M M ∑ j=1 v i | (8)

Computing Influence Distribution D I Definition 7 (Influence Distribution D I ). When component C i occurs fault, the influence it has for physical variable v j is defined as D I (i, j).

The direct way to get D I is the use of simulation. If enough simulation scenarios covering the range of all the variables are provided, D I can be exactly constructed with simulation results. However, the use of simulation is not practical for the non-frequently happening accidental fault. To make a compromise between the computation time and the precision, instead of simulation, we compute D I by using the Influence Graph. This approach may sacrifice some precision, however, the experimental results in Sec. 5. will prove its accuracy and efficiency for the accidental fault problem.

When a component's output physical variable violates functional constraints, either because it encounters an accidental fault, or because its dependent components has spreadable fault. The propagation of fault is related to system's architecture that:

• If C i has encountered an accidental fault, it will probably generate an output violation. The reason why it is not a definitive violation is that in one simulation, the required-context for violation may not happen. This probability is defined as D A .

• If C i depends on C j (C i directly or indirectly takes C j 's output as input), and if an accidental fault occurs on C j , C i will probably have an output violation. It is not a definitive violation, because either in one simulation, the required-context for violation may not happen; or the intermediate component's design has fault-tolerance consideration. This probability is defined as D B .

Algorithm 2 (Influence Distribution Algorithm). As D A and D B are independent, we have D I = D A + D B . D A and D B can be computed using G I .

• If variable v j does not depend on component C i (C i has no path to v j ), D I (i, j) = 0

• If v j directly depends on C i , D A (i, j) = G I (C i , v j )
• If v j indirectly depends on C i , assume there are P paths from C i to v j . Let O ik be the k th output of C i , and S(C i ) is the index of C i 's successor component on current path. The recursive computation is as follow:

D B (i, j) = P ∑ k=1 G I (C i , O ik ) • D I (S(C i ), j) (9)
Eq. 9 describes system's fault propagation and fault exposition. If some faults occur, the most probable faulty component should be the one that exchanges the most often the data/information. That's why there is always an accumulation.

Computing Emission Probability Matrix (M E ) Algorithm 3 (Emission Matrix Algorithm). For the HMM of variable v, if the state representing C i is failing, we use the following algorithm to compute the probability of generating the observations V I , V C and V N .

For V I , M E should be a function of D I . According to our test, we found that function y=x is good enough to give out a relatively accurate result, as shown in Eq. 10.

M E (C i ,V I ) = D I (C i , v) (10)

For V C , if v violates several functional constrains with other variables (v i ), the occurrence time of v i in these constraints is n i , and the occurrence time of v is n. The coupling violation is computed as follows:

M E (C i ,V C ) = nD I (C i , v) ∑ i n i D I (C i , v i ) (11)
The emission probability from state to V N is:

M E (C i ,V N ) = 1 -M E (C i ,V I ) -M E (C i ,V C ) (12)
Since the only valuable information/entropy in fault localization method will become maximized only when a fault happens, therefore it is difficult (even impossible) to find directly a statistical distribution for those fault-free behaviour. In this case, in order to not introduce some new assumption, we choose to use the HMM's mathematical property (emission matrix must be semantically exclusive and the sum should be 1) to present the normal state-observation pair by Eq. 12.

Obviously, the healthy state (N F ) will lead to

M E (N F ,V N ) = 1, M E (N F ,V I ) = 0 and M E (N F ,V C ) = 0.

Online Locating Accidental Fault

If we choose T S simulation time points for all the M HMMs, we observe T S • M sequences, with which the most probable system behaviour can be derived. By computing the statistic of each state, the state with maximum probability is located as the fault source. To online locate accidental fault for time point t, we use the observed sequences in the past time of t according to the feature of markov process. However, when t is too long, we might have huge quantity of observed sequences, which makes the computation time increases. To solve this problem, a sampling window W is introduced to limit the length of t.

Example 4. At time point 5, The observed sequences for 3 variables are:

Seq 1 : N F F 1 F 1 F 2 F 1 Seq 2 : F 1 F 1 F 1 F 3 F 1 Seq 3 : N F F 1 F 1 F 2 F 1
The state with maximum probability is F 1 , which means component C 1 is most probably the fault source.

EXPERIMENTAL RESULTS

Test Bed

Since it is costly to generate test data for real system, and it is less convincing by replaying a sample, we design a specific test bed to evaluate the method's accuracy and efficiency by generating a large quantity of simulated use cases. Each use case includes: the system architecture which defines the components and physical variables, and their interconnections; the failure probability of each component; the functional specification corresponding to each physical variable or some group of them; the running time counter after which the system will stop producing output.

The method assumes that: Each component in the system has a chance to fail. This probability can be 0; All functional constraints are based on variable's value itself, and for simplification, they are all range constraint, which means they delimit only the min/max value of the variables.

If an accidental fault occurs, the test bed will give out an out-of-range value for this component's output. This emulates how a system falls into fault, whatever the model is.

The test bed will extract its architecture and give each component a random low probability of falling into accidental fault as the prior knowledge of device's fault parameter. Each component's input and output will be allocated to a variable by the test bed. It guarantees that the interconnected ones share the same variable. The variable will be associated with a random range, which is the functional specification. If a device is more probable to fail, the test-bed-generated value for its entire output variables will be more probable to go out of the defined range.

The approach will use the generated data and the functional specification to locate the most probable component as the accidental fault source if there is some violation observed. The test bed will then compare this computed conclusion with the initial context to deduce whether the method is efficient.

Experiments

Our test bed has generated 1000 test cases to evaluate the method's performance in terms of accuracy. The criterion that impacts the accuracy is the complexity of system's architecture. This can be measured by component's average input & output number (Fig. 9), and component number. (Fig. 10). We generate different scale test groups from 1 to 50 inputs/outputs or components. Each group has 20 test cases. The test results show the max/min/average accuracy ratio in each group. We find out this method is more sensible to the average input & output number, while more scalable to component number. This method deals with the accidental fault localization for middle-range systems with accuracy superior to 95%. The computation of the HMM parameters by resolving linear system (the influence layout algorithm) and eventually optimising non-linear system (the initial matrix without complete prior knowledge) consumes time, ranging from seconds to several minutes according to the system's complexity. However, it computes only once before the fault localization process begins, because the HMM's initialization is architecture-dependent-only. Once the HMM is precomputed, the fault localization algorithm is very fast and depends only on the observation sequence length. Since the

Input / Output

Helicopter Control System Case Study

After certifying statistically the method's performance using the test bed, we rely on a classical Simulink teaching model of simple helicopter control system [START_REF] Cazzolato | Automatic Control II -2DOF Helicopter Tutorial & Lab[END_REF] to evaluate, whether the proposed approach works also well for real system. A complete mathematical model, including propeller dynamics, forces generated by the propellers, static and dynamic friction of the bearings, etc. is an overkill for method proofing objectives, therefore a simplified version is chosen. This helicopter is a linear 4 th order system, where the 6 control matrices are pre-defined.

In order to introduce the accidental fault into the test, we modify the Simulink model to have some components generating faulty outputs at some given time point according to its original functionality. These components will have a computed failure probability which is based on the ratio between failure count and total simulation time tick. The total simulation time is 10000, while the sampling window size is 1000. The fault localization method gives a good accuracy throughout the test, as shown in Fig. 11: the average accuracy degrades with the widening of sampling windows, but once the sampling window is selected, the accuracy stabilizes and the total accuracy is superior to 90%.

Discussion

This method needs less complete specification to derive fault analysis conclusion, because it uses architecture concept. This is an advantage for analysing old-fashioned system, when people focus mainly on conception but not formal specification.

At the very beginning of this paper, we reasonably assume there is at most one accidental fault in the whole system at

Helicopter Control System

Aver Ratio: Heli 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 8 000 9 000 10 000 In this situation, the computational complexity is only O(n), which is scalable. If we consider there might be n accidental fault at time t, it is O(2 n ) complicated in space and time, which makes it difficult to scale for large system with complex architecture. A compromise can be applied in practice if we pre-know that at most only K component will encounter accidental fault at the same time. Therefore the complexity reduce to O(C (n,1) +C (n,2) + ... +C (n,k) ).

Simulation

A weak point of this method is the quantification of some hypothesis based on the influence distribution D I . This is also why we find in the experiment, when the complexity of system architecture increases, the method's accuracy degrades. To solve this problem, we should introduce an approach which makes good compromise between architecture-based HMM parametrization and observation sampling learningbased HMM initialization. This is our ongoing work.

CONCLUSION

This paper proposes a novel approach to do online analysis of accidental fault localization in dynamic systems by using HMM. HMM can be used as an abstraction of real system, and emulates real system's behaviour. Inspired by the principles of FTA and of maximum entropy in Bayesian probability theory, we propose the algorithms to estimate HMM's parameters, avoiding to pass through learning task, because the learning data for accidental fault in real system is difficult to obtain. Once HMM is completed, by observing output sequence within the sampling window, we can online estimate the source of accidental fault. We design a specific test bed to generate large quantity of test cases. The experimental results have assessed the accuracy and efficiency of our approach. The accuracy of accidental fault localization is superior to 95% for normal scaled systems. Meanwhile, we apply the approach to a simple helicopter control system case study, which shows the accuracy is superior to 90%.

Figure 1 .

 1 Figure 1. Cyber-Physical System with Control Modes

Figure 3 .

 3 Figure 3. HMM Diagnostics and Prognostics

• 1 •Figure 4 .

 14 Figure 4. A Two States Hidden Markov Model

  |ω i -ω j | = 0 will be generalized to min( N ∑ i, j=1 |ω i -ω j |). This can be solved because N ∑ i=0 M I (i) = 1.

Figure 7 . M E Algorithm 4 . 5 . 1 .

 7451 Figure 7. M E Algorithm

AccuracyFigure 9 .

 9 Figure 9. Accuracy by Input Number

AccuracyFigure 10 .

 10 Figure 10. Accuracy by Component Number sampling window is bounded, this method gives a good response to the online accidental fault localization problem.

Figure 11 .

 11 Figure 11. Helicopter Control System Case Study Result time point t. In this situation, the computational complexity is only O(n), which is scalable. If we consider there might be n accidental fault at time t, it is O(2 n ) complicated in space and time, which makes it difficult to scale for large system with complex architecture. A compromise can be applied in practice if we pre-know that at most only K component will encounter accidental fault at the same time. Therefore the complexity reduce to O(C (n,1) +C (n,2) + ... +C (n,k) ).A weak point of this method is the quantification of some hypothesis based on the influence distribution D I . This is also why we find in the experiment, when the complexity of system architecture increases, the method's accuracy degrades. To solve this problem, we should introduce an approach which makes good compromise between architecture-based HMM parametrization and observation sampling learningbased HMM initialization. This is our ongoing work.

We will improve the concreteness of the influence distribution by introducing heuristic algorithms and experiment the approach on more complicated use cases in future work. Meanwhile, we will introduce an approach which makes good compromise between architecture-based HMM parametrization and observation sampling learning-based HMM initialization. The later is capable be generalized to more applications, e.g. for detecting and locating design faults in systems.


Trends in avionics switched Ethernet networks Christian Fraboul and Jean-Luc Scharbarg Université de Toulouse IRIT INP-ENSEEIHT France

The AFDX (Avionics Full DupleX switched Ethernet) which has been standardized as ARINC 664 is based on Ethernet technology. Using full duplex links eliminates the inherent indeterminism of vintage Ethernet due to frames collision, but it shifts in fact the problem to the switch level where various flows enter in competition when using the same output port. Thus main problem is due to the indeterminism of the switching mechanisms and a network designer must prove that no frame will be lost by the AFDX (no switch queue overflow) and must evaluate the end-to-end transfer delay through the network (guaranteed upper bound and distribution of delays) according to a given avionics applications traffic.

AFDX specific assumptions deal with the static definition of avionics flows called Virtual Links (VL) that have to respect a bandwidth envelope (burst and rate) at network ingress point. Transmitting an Ethernet frame from one end system to other ones is based on a (mono-emitter and multi-receiver) VL identifier which is used for deterministic routing of each VL (the switch forwarding tables are statically defined after allocation of all VL on the AFDX network architecture). A VL definition includes the Bandwidth Allocation Gap (BAG) value, the minimum and the maximum frame size. The BAG is the minimum delay between two consecutive frames of the associated VL (sporadic flow). Thus the BAG and the maximum frame size values guarantee an allocated bandwidth for each VL.

The end-to-end delay analysis of a path (multicast link) of a given VL has to take into account all the possible scenarios (due to random conflicts with other VL). The smallest possible value of the end-toend delay corresponds to the scenario where the VL emits a frame with minimal length which never waits in output ports. The highest possible value of the end-to-end delay corresponds to the worst-case scenario (which is a key point for the certification of the avionic network). In the general case, finding this worst-case scenario requires an exhaustive analysis of all the possible scenarios. Such an exhaustive enumeration (as undertaken by the Model Checking approach) is limited by the combinatorial explosion problem, since the number of possible scenarios is huge, due to the number of VLs (around 1000) of industrial configuration [START_REF] Charara | Methods for bounding end-to-end delays on an AFDX network[END_REF].

An alternative solution is the computation of a safe upper bound of the end-to-end delay, based on a modelling of the configuration which over-estimate the traffic and/or under-estimate the service offered by the network (pessimistic assumptions). Two approaches have been proposed for computing a pessimistic safe upper-bound of the end-to-end delay of any VL of an industrial AFDX configuration.

-The first one is based on Network Calculus theory. It has been applied and is optimized for AFDX, since it integrates the serialisation of frames on links. It has been used for the certification of the A380. It gives safe (but pessimistic) upper-bounds on the end-to-end delays of flows [START_REF] Li | Improving end-to-end delay upper bounds on an AFDX network by integrating offsets in worst-case analysis[END_REF].

-The second one uses the Trajectories theory that has been developed to get deterministic upperbounds on end-to-end response time in distributed systems. It computes the maximum workload faced by any frame of a given flow on its trajectory. It also gives safe upper-bounds which are slightly tighter compared with the network calculus approach [START_REF] Bauer | Improving the worst-case delay analysis of an AFDX network using an optimized Trajectory approach[END_REF].

Moreover the distribution of the end-to-end delay between its smallest and its highest possible values is also valuable when prototyping the whole system. This distribution can be obtained thanks to a simulation approach [START_REF] Charara | Methods for bounding end-to-end delays on an AFDX network[END_REF].

The following figure summarizes the end-to-end delay analysis global approach. This delay is always between a minimum and a maximum value. Most of the time, the exact maximum value cannot be computed and it is lower-bounded by the maximum observed value and upper-bounded by the computed safe upper-bound. Measurements on real AFDX networks (or simulated configurations) show that the end-to-end delays are always much smaller than the computed upper-bounds. Thus the network is over-dimensioned.

However it has been shown that both worst-case approaches (Network Calculus and Trajectories) provide tight upper bounds on typical industrial configurations. These two results seem contradictory but they are not: the worst-case delay for a given VL corresponds to very rare scenarios [START_REF] Li | Analysis of the pessimism of the Trajectory approach for upper bounding end-to-end delays of sporadic flows sharing a switched Ethernet network[END_REF].

So a first trend for minimizing the worst-case delay is to minimize the number of scenarios by taking into account the fact that some of the flows are synchronised. Up to now we considered that all the VLs are independent. This is true for VLs generated by different end systems. This is not true for VLs generated by the same end-system. Indeed each end-system implements a scheduling which can generate constraints on the generation times of the VL frames. Some work has been conducted in order to integrate these constraints in the worst-case delay analysis. It seems to be a promising direction in order to limit the over-dimensioning of the network [START_REF] Li | Improving end-to-end delay upper bounds on an AFDX network by integrating offsets in worst-case analysis[END_REF].

A second trend for a better utilization of available resources is to integrate Quality of Service (QoS) mechanisms in the network. Indeed, for future aircraft, the addition of other type of flows (audio, video, best-effort) on the AFDX network is envisioned. These new flows have different timing and criticity constraints. Thus, they have to be differentiated and handled by a QoS-aware AFDX switch which implements other service disciplines, such as static priority queueing or weighted fair queueing. The issue is to integrate efficiently these QoS mechanisms in the worst-case delay analysis [START_REF] Bauer | Applying Trajectory approach with static priority queueing for improving the use of available AFDX resources[END_REF].

A third trend concerns the model-checking approach. One valuable feature of this approach is to be able to exhibit a worst-case scenario. Obtaining such a scenario on a medium-size configuration can brings interesting information on worst-case scenario features. Thus work has been done in order to mitigate the combinatorial explosion problem [START_REF] Adnan | An improved timed automata approach for computing exact worst-case delays of AFDX sporadic flows[END_REF].

Finally, a fourth trend deals with the sporadic characteristic of avionics flows that could be described by a probabilistic model. This leads to a probabilistic analysis of the worst-case delay of flows as it has been done for a preliminary study in the context of probabilistic Network Calculus [START_REF] Scharbarg | A probabilistic analysis of end-to-end delays on an AFDX avionic network[END_REF].


P Brequigny

E Pacaud

C Mounaïm-Rousselle

COMBUSTION OF A SYNGAS FROM SEWAGE SLUDGE GASIFICATION ENRICHED WITH AMMONIA IN A SPARK-IGNITION ENGINE

Keywords: combustion, engine, alternative fuel, ammonia, syngas

Syngas (Synthetic Gas), also known as producer gas or wood gas, is a gaseous fuel which could be produced by the gasification of biomass. It is mainly composed of hydrogen and carbon monoxide with a smaller share of methane, all diluted by nitrogen and carbon dioxide. Although it contains carbon in its composition, it is considered as a low or zero carbon fuel as soon as it is made from biomass, which makes it a strong candidate for reducing the global warming impact of combustion engines. This work focuses on the combustion development and performances of a Spark-Ignition engine, fuelled with a syngas enriched with ammonia, from a nitrogen-rich sample, typical of ammonia-enriched sewage sludge. Results

show that, by replacing part of the syngas by ammonia, even as expected the combustion development is slowed down, the indicated work increases. Last exhaust emissions measurement shows a decrease in CO and CO2 as a function of ammonia addition in the fuel but a slight increase of NOx.

INTRODUCTION

The Intergovernmental Panel on Climate Change IPCC (https://www.ipcc.ch/) report has brought clear insight concerning climate change with the estimated global warming scenarii. The most optimistic forecast expects a 2°C global warming for the next century. To limit it as much as possible, the global emissions of the Greenhouse Gases (GHG) must significantly decrease in the next few years. Electricity and Heat production along with Agriculture Forestry and other Land Use (AFOLU) contribute to half of these world's emissions.

The agro-industrial feedstock such as the municipal sewage sludge is a source of renewable gases making them attractive fuels. These gaseous fuels can be obtained by different process as hydrothermal gasification (375-500°C), gasification (800-1000°C) and methanization (40°C). The choice between wet and dry processes depends on the nature of the feed and its water content. Among these processes, gasification takes place in the presence of water and is especially suited for the valorization of wet feedstock (water content >50%), such as wet animal manures and municipal sewage sludge (SS). Gasification or Pyro-gasification is well suited for biomass with low water content, such as agricultural and forestry waste but wet wastes can also be used if a drying step is applied on them. As demonstrated by Wu et al. [1], GHG emissions from agriculture can be reduced by changing the current use of manure by using gasification. This process shows an improvement regarding waste's life cycle and a GHG emissions reduction turning biomass into energy supply. By using such fuel in a cogeneration process or an electricity power unit, it would enable locally better waste management while providing sustainable energy and could potentially reduce GHG emissions from both the energy and agricultural sectors.

The purpose of a gasification system is to transform solid fuels (crude biomass or char obtained from pyrolysis or hydrothermal carbonization) into combustible gaseous mixtures. The gaseous products, mainly composed of hydrogen (H2), carbon monoxide (CO), methane (CH4), nitrogen (N2) and carbon dioxide (CO2) forming syngas, can be used directly as a gaseous fuel for thermal energy converter. One gasification challenge is to keep the gas free of harmful components (i.e. nitrogenous species) prior to subsequent combustion and power generation. As a waste-to-energy process, biomass thermal conversion competes with landfilling and methanization, but the carbon utilization of the syngas road is higher than landfilling [2] and can be in some case better than the biogas road [3].

Methanization is limited where livestock manure can be considered as waste when it is in surplus, especially in areas with structural nitrogen surplus. Indeed, the nitrogen concentrated in the digestate limits its spreading in this situation because of possible ammonia emission and soil and water contamination.

Yet, the thermal conversion remains challenging because the properties of the producer gases, thus its combustion potential, vary significantly with the feedstock characteristics (chemical composition, moisture content, structure, reactivity, physical properties, impurities, etc.) [4][5][6][7], the gasification reactor type [8] and the key operating parameters (temperatures, reaction atmosphere, stoichiometric ratios, gas flow rates and heating rates) [9][10][11]. During the gasification, different impurities (nitrogen-impurities, sulphur-impurities, and chlorideimpurities) may be produced favourably [12,13], depending on their quantity in the original sample [14]. Agroindustrial manures and SS have similar properties with higher nitrogen (N) and water contents produced in large quantities, and therefore are considered good candidates for energy production. Thus, many efforts have been made to investigate the N conversion during sewage sludge pyrolysis [15,16]. During the thermal conversion, nitrogen is released as nitrogen-containing volatiles including NOx precursor gases such as ammonia (NH3) and hydrogen cyanide (HCN). Besides, the composition of these volatiles is dependent on the procedure applied and temperature range. The nitrogen conversion and the emission of nitrogenous compounds (NH3, HCN) or N-Tar yields production is strongly dependent on the temperature [15], the heating rate, and the residence time [16]. The choice of the gasifying agent (e.g. steam or CO2) is also affecting the quality of the gas (hydrogen or methane-rich gas with less CO2 and tar formation at the exit). Hence, such feedstocks provide favourable conditions for the yield of N-impurities such as ammonia. However, in most processes, especially engines for Combined Heat and Power (CHP), these undesired yields are avoided as much as possible to obtain a "purer" syngas, i.e. mostly composed of H2 and CO.

The use of syngas in thermal energy converters dates back to World War II when gasoline shortages stimulated the conversion of vehicles to fuel derived from wood gasification. A renewed interest appeared last decades with a use of syngas in CHP application. Despite being a well-established technology, researches on the characteristics of syngas fuelled energy converters are limited especially considering the syngas composition impact. As underlined in the review of Fiore et al. [17]: "difficulties in predicting the actual flame speed essentially derives from the wide variability of syngas composition, which results from a number of factors related to its production technology" or the nature of the feedstock as underlined above. The questions are therefore what could be the impact of the "raw" syngas composition on the combustion as a function of the feedstock type and how the presence of impurities in the gas would affect the engine performances (possibly in a CHP system). If the presence of these impurities does not affect the gasification process itself, nor the thermal conversion via combustion, the overall cost of resource recovery would decrease due to one less step. It is, nonetheless, necessary to have a flexible and robust installation to provide the highest efficiencies whatever the syngas compositions.

As for application, syngas is nowadays successfully used in thermal converters based on spark-ignition (SI) engines. However, since those kinds of engines are usually designed and optimized for gasoline or natural gas, specific arrangements are required to operate on syngas. The important variability of composition exacerbates this issue decreasing operation stability and strengthen the need of optimizing operating parameters through prevention and regulation systems. Moreover, the use of syngas generally results in a decrease in engine power, which can be compensated by increasing the compression ratio as much as possible, as highlighted by Szwaja et al. [START_REF] Szwaja | 18th World Hydrog. Energy Conf[END_REF][START_REF] Szwaja | [END_REF] for a synthesis gas representative of sewage sludge gasification. As underlined by Fiore et al. [17], a significant number of studies, both numerical and experimental deals with the use of syngas in SI engines. However, most of them focus on the knock limit strongly affected by the syngas composition. Studies on the impact of the syngas composition in SI engine by considering a complete syngas composition (not only H2/CO blends) are quite sparse.

Bhaduri et al. [20] fueled a compression ignition engine in homogeneous combustion mode by connecting directly a gasifier, which results in composition variation as a function of time of the gasification process itself. Recently, Rabello de Castro et al. [21] studied 3 different syngas compositions previously identified by Bridgwater [8], in a dual-fuel compression ignition engine. They showed that: i) H2 content has a strong influence on the Heat Release Rate as well as on the combustion phasing and duration; ii) the presence of CO2 in the syngas plays an important role in reducing NOx emissions but too much CO2 can lead to poor efficiency and emissions increase; iii) the combustion development can be empirically predicted by fundamental properties of the syngas composition such as laminar flame speed or adiabatic flame temperature. One other interesting study was proposed by Tsiakmakis et al. [22] with the use of three different compositions in SI engine coming from three different feedstocks namely olives, peaches and grapes showing engine performances but pollutant emissions were not measured. They blended these compositions with propane and compared it with pure propane showing that increasing the syngas content leads to reduced in-cylinder pressures, heat release rates and power output but that stable operation is ensured with a power output reduction less than 10% for mixtures of 55% w/w producer gas and 45% w/w propane compared to neat propane. As a result, the impact of impurities on engine operation is not well covered by the literature currently and considering that N impurities can lead to NOx emissions at the exhaust, this requires quantification.

Moreover, ammonia recently gains a renew interest has a potential zero-carbon fuel in internal combustion engine especially in Spark-ignition (SI) ones. Indeed, despite his unfavorable combustion properties [23], stable operation were achieved with neat ammonia in SI engines with different architectures [START_REF] Grannell | Proc. ASME Intern. Combust. Engine Divivision 2009 Spring Tech. Conf[END_REF][START_REF] Mørch | [END_REF][26][27][28][29]. It could be also noticed that one of the first use of ammonia in engines dates back to WWII in Belgium, where the bus fleet operated with the first dual fuel engine ever reported: coal gas (so syngas) composed of 50% H2 directly injected in the combustion chamber filled with ammonia [30].

The objective of this study is therefore to consider this ammonia content no longer as an impurity but as part of the fuel. For this purpose, a syngas composition representative of sewage sludge gasification (likely to contain ammonia) is selected and investigated in a Spark-Ignition engine in terms of performances and emissions. For reference, the syngas is first compared to pure methane. The second part of the present paper studied the effect of blending ammonia with syngas on the engine performances and emissions to assess its impact either considered as an impurity or a co-fuel by varying the ammonia share in the blend.

EXPERIMENTAL SETUP

Engine characteristics

The experiments were conducted in a research single cylinder SI engine. All specifications are presented on the Table I. For all operating conditions, the engine is driven by an electric motor at a fixed engine speed of 1400 rpm. The optical encoder placed in the main crankshaft enables the monitoring of the angular position with a 0.1 Crank Angle Degree (CAD) resolution. The bench is described in more details by Mounaïm-Rousselle et al. [29]. The in-cylinder pressure is measured using a Kistler piezoelectric pressure transducer (6045A). Intake and exhaust temperature and pressure are monitored using K-type thermocouples and piezoresistive absolute pressure transducers respectively. The absolute in-cylinder pressure is obtained by its equalization with the average absolute pressure at the inlet, Pin, at 20 CAD after the intake valve opening. The charge duration for the ignition coil was set to 2ms. For all the data presented below, the Spark Ignition Timing (SIT) was optimized to obtain the maximum Indicated Mean Effective Pressure (IMEP), with a covariance, COVIMEP , below 5%.

All gaseous flows are measured and controlled using Brooks thermal mass flowmeters with a 0.7% precision to ensure control of the equivalence ratio below a 1.5% variation. All gases are preheated to the intake temperature of 30°C and premixed in a plenum before their introduction into the combustion chamber. The engine exhaust emissions were measured with the Horiba MEXA 7100HEGR. This exhaust gas analyser measures CO, CO2, NOx, O2 and unburned hydrocarbon (HC). As these results are given in dry share (except for HC), a calculation considering the share of water vapour in the exhaust enables to readjust the content of each gas. A global scheme of the experimental setup is displayed in Fig. 1.

Figure 1. Layout of the experimental setup

Syngas composition

A syngas composition from the gasification of sewage sludge was chosen to evaluate its combustion characteristics. This kind of feedstock is usually nitrogen-rich and able to produce ammonia as impurity when gasified. The composition chosen for this study was the one from Szwaja et al. [START_REF] Szwaja | [END_REF] which was configured to be in an optimal gas production condition. This syngas composition is displayed on Table II and will be blended with ammonia to evaluate the impact of ammonia in combustion development in a spark-ignition engine.

From Eq. 1, air-fuel ratios and mole fraction of each gas based on the equivalence ratio are calculated for all conditions.

Experimental conditions

For this study, methane was firstly used as a reference fuel with three equivalence ratios as displayed in Table III. These values provide a relevant comparison with pure syngas in the same conditions, and then with ammoniaadded syngas, for two different intake pressures. The amount of ammonia in the syngas was incremented by 2.5% up to 10% then with a last point at 15%, to observe the impact of ammonia 'impurity' on the syngas performance. The Net Heat Release Rate (HRR) is calculated from the in-cylinder pressure as follows:

𝑑𝑄 𝑑𝛼 = 𝛾 𝛾 -1 . 𝑃. 𝑑𝑉 𝑑𝛼 + 𝛾 𝛾 -1 . 𝑉. 𝑑𝑃 𝑑𝛼 (1)
Where 𝛾 is the heat capacity ratio, 𝑃 the in-cylinder pressure, 𝑉 the in-cylinder volume and 𝛼 the CAD. Only the net heat release rate is calculated, heat losses were not estimated. The HRR is initially computed with a constant heat capacity ratio to estimate the share of burnt and unburnt gases as a function of the crank angle. Then, the process is repeated with 𝛾 depending on the gas composition and temperature at each CAD.

The indicated efficiency is computed as follows:

𝜂 = 𝑊 𝑄 = 𝐼𝑀𝐸𝑃. 𝑉 𝑚 . 𝐿𝐻𝑉 (2)
where 𝑊 corresponds to the indicated work, 𝑄 the energy content of the mixture, 𝐼𝑀𝐸𝑃 the indicated mean effective pressure, 𝑉 the displaced volume, 𝑚 the mass of the fuel and 𝐿𝐻𝑉 the low heating value of the fuel.

Two methods have been established regarding the combustion efficiency. The first one computes the efficiency by taking into consideration the unburnt or partially burned exhaust gases with this equation as follows:

𝜂 = 1 - 𝑉 ̇ 𝐿𝐻𝑉 + 𝑉 ̇ 𝐿𝐻𝑉 𝑉 ̇ 𝐿𝐻𝑉 (3)
Where 𝑉 ̇ , 𝑉 ̇ and 𝑉 ̇ are respectively the volume flow rate of hydrocarbon, monoxide carbon and fuel multiplied by their Low Heating Value in MJ/L. This method must include all unburned or partially burned gases to be totally accurate. Since not all these gases can be assessed from the analyser, this method is only an estimate of the combustion efficiency considering only the hydrocarbon and monoxide carbon reactions.

The second method is based on oxygen balance between intake and exhaust and computed as follows:

𝜂 = 1 - 𝑂 𝑂 (4)
Where 𝑂 and 𝑂 are the volumetric flow rate of oxygen at the exhaust and at the intake respectively.

Kinetics simulation setup

Numerical simulations were carried out using the premixed laminar flame-speed calculation module PREMIX of ANSYS CHEMKIN-PRO [27] to estimate the laminar flame speed and adiabatic flame temperature in engine conditions. Simulations were carried out with an average number of 1000 meshes on a 10 cm grid, a curvature of 0.1, and a gradient of 0.05 with 5 continuations. The selected mechanism is the one of Okafor et al. [31] since it is one of the only ones able to simulate syngas/ammonia blend. This mechanism is based on the GRI-Mech 3.0 [32] for the carbon chemistry, well suited for methane and natural gas and on the mechanism of Tian et al. [33] for ammonia chemistry. It consists in 59 species and 356 elementary reactions and was validated on laminar flame speed of methane/ammonia mixture with an ammonia energy share up to 30% of the total energy content, at atmospheric conditions, i.e 1 atm and 298 K, and equivalence ratios ranging from 0.8 to 1.3. In our case, the 90% syngas/10 % ammonia blend corresponds to an ammonia energy share of 33% and the 15% ammonia blend corresponds to 53% of ammonia energy share.

RESULTS AND DISCUSSION

Syngas versus methane

Figure 2 2 displays the in-cylinder pressure of methane and syngas for three equivalence ratios. As expected, the maximum of pressure obtained with syngas as fuel is lower than for methane only, not really affected by the equivalence ratio, where the maximum of pressure is around 42 bar.

Figure 2:

In-cylinder pressure evolution for syngas compared to pure methane for three equivalence ratios at 1 bar intake pressure.

As consequence, the IMEP for pure syngas is lower than for methane only, respectively 5.5 bar and 9 bar as it can be seen in Fig. 3, due to the presence of high amounts of inert gases (N2=53% and CO2=15%) which induces lower LHV and lower energy input. These results are in good agreement with those from Szwaja and Cupial [START_REF] Szwaja | 18th World Hydrog. Energy Conf[END_REF].

The increase of the intake pressure (from 1 to 1.2 bar) enables a higher IMEP thus showing potential for boosted operation especially due to the knock resistance of syngas compared to methane. This way, even though methane combustion is more efficient than syngas, the increase of intake pressure could be a way to compensate the performance depletion provoked by syngas. For both fuels, the combustion was stable with COVIMEP between 0.74% and 1.5% for methane and 0.9% on average for syngas. From Figure 3, it can be noted the decreasing trend of the indicated efficiency can be observed with both gaseous fuels as the air/fuel ratio increases, with also lower values reached for syngas fuel than methane only (36% and 38% at ER=1 and 36% and 40% at ER=0.9, respectively).

The heat release rate comparison between syngas and pure methane as a function of 𝜙 is shown in Figure 4.

Its evolution tends to follow the in-cylinder pressure one due to LHV and energy input difference (about 0.82 kJ for syngas vs. 1.20 kJ for CH4): a maximum peak at 17 J/CAD for syngas at ER=1.1 against 76 J/CAD for methane at 𝜙=1. However, the HRR evolution is wider for syngas than methane, which means that the combustion duration is longer. This difference is highlighted in Figure 5 where the three different combustion phases are given : from the start, i.e. early flame kernel development (CA10-SIT), the mid, i.e. self-sustained propagation (CA50-CA10) to the end, i.e. post-flame combustion (CA90-CA50), with the variable CAXX, representing the crank angle degree when XX% of mass is burned. As shown in this bar chart, the syngas has a longer combustion mainly during the first phase (20 CAD for syngas against 15 CAD for methane only). As highlighted in [34,35], the laminar flame speed of a similar syngas composition is much smaller than for methane. As the laminar flame speed is one of the main parameters driving the combustion process in Spark-Ignition engine, it is one of the main reasons for the slower combustion explained by the dilution effect of nitrogen and carbon dioxide within the syngas. This figure also shows the negligible impact of the equivalence ratio on the different combustion phases for the syngas in contrary with the methane whose combustion duration decreases when increasing the equivalence ratio up to 1.1 corresponding to the maximum laminar flame speed. The emission comparison between methane and pure syngas is displayed in the Figure 6. As expected, the CO2 emissions for syngas (17%) is twice that of methane (8%), due to the presence of CO2 in syngas composition (15%). NOx and unburnt HC are lower for syngas, respectively 50ppmv and 720ppmv, than for methane (1500ppmv and 2130ppmv), at 𝜙=1. The amount of NOx produced with methane as fuel is due to the highest temperature, favouring thermal NOx formation. The specific composition of the syngas tends to provoke similar effect than the dilution by exhaust gas recirculation (EGR), preventing thermal NOx production due to the decrease of the combustion temperature. By considering HC and CO emissions, the difference induced by the two fuels might be related to the lower carbon content of the syngas. As a function of the equivalence ratio, no real impact can be noted for the syngas; contrary to the case of methane, as already well studied in literature [36]. NOx emissions increase in lean side (4300 ppmv at ER=0.9 versus 320 ppmv at ER=1.1) as expected but HC and CO emissions respectively follow the opposite trend. show the in-cylinder pressure and HRR evolution as a function of ammonia addition in syngas at 1.2 bar intake pressure and 𝜙=1.0. Their peaks values do not seem affected by the presence of ammonia in the fuel, only a small change on combustion duration can be distinguished. Since IMEP is the integral of the incylinder pressure, more indicated work is then obtained when ammonia is added due to the increase of LHV and energy input (Table IV) , as it can be seen in Figure 8, for 1 bar and 1.2 bar intake pressure. According to this figure, the addition of NH3 to the syngas' mixture increases the IMEP. As expected, and already highlighted for pure syngas, the 20% intake pressure increase enables to increase the IMEP of more than 1 bar, more than 22%, without any knock occurrence. Figure 10 shows the evolution of indicated efficiency as a function of the ammonia content in syngas: for both intake pressures, a slight decrease can be observed with the increase of ammonia content from 𝜂 =36% for pure syngas until 35% when 15% ammonia is added. This decrease remains yet restrained without significant tendency with the intake pressure increases.

Figure 10: Indicated efficiency as a function of ammonia content in the blend for both intake pressures -ϕ=1

The addition of ammonia in the blend has an impact on the different combustion durations as highlighted in Figure 11. The greater the concentration is, the longer the start of the combustion will last, mainly due to the low flame speed of ammonia as demonstrated in Lhuillier et al. [23,37]. To confirm this analysis, CA10-SIT and CA50-CA10 are plotted against the laminar flame speed estimated by kinetics OD simulations with the in-cylinder pressure and temperature at SIT conditions as initial conditions. The Figure 13 displays the evolution of the exhaust emissions as function of the ammonia content. Once ammonia is introduced into the fuel, the NOx emissions increase drastically: from 50 ppmv without ammonia to 2000 ppmv with just only 2.5% of ammonia in the fuel. However, after this jump, NOx emission is not sensitive to the ammonia content and remains around 2100 ppmv, due to the different kinetic mechanisms of nitrogen "fuel road" [38], involving a set of recombination reactions between ammonia and oxygen to form NOx. Indeed, the adiabatic flame temperature (estimated with kinetics simulations), one parameter to enhance thermal NOx production, is not really affected by this small addition of ammonia: about 2130 K constant from 0 to 7.5% of ammonia and a slight increase up to 2165 and 2186 K for 10 and 15%NH3 respectively. The combustion efficiency might explain the stabilisation of NOx emissions as the amount of unburned ammonia tends to rise which prevent from further oxidation and thus NOx production. Since the HORIBA analyser cannot measure ammonia at the exhaust, this statement is still uncertain if this stabilisation is caused by a mitigation between the high NOx concentration and oxidation or if the combustion of ammonia is incomplete. The Figure 14 shows the evolution of the combustion efficiency with the addition of ammonia content by using Eq. 4 and 5. As observed, the combustion efficiency calculated from pollutants (Eq. 4) seems stable through the entire campaign with a 98.2% average efficiency at 1 bar intake and a 98.3% average efficiency at 1.2 bar intake.

This result proves the low impact of the exhaust gases HC and CO on the combustion efficiency regardless of the intake pressure and ammonia content. The second estimate (Eq. 5) clearly indicates a decreasing trend with the addition of ammonia from 94% with 0% NH3 to 87% with 15% NH3 at 1 bar intake pressure, highlighting that a larger part of the fuel is unburned as the ammonia is added. Unlike the first approach, the second one considers the unburned part of ammonia in the exhaust gas. As seen above, since the combustion efficiency is not affected by the HC and CO, its decrease is mainly due to the rise of unburned part of ammonia as the share of ammonia in the syngas increase. Also, increasing the intake pressure tends to improve the combustion efficiency from 86.9% with 15%NH3 at 1 bar to 93.9% with 15% NH3 at 1.2 bar. Hence, a fewer quantity of unburned ammonia might be present in the exhaust gas at higher intake pressure. The O2 combustion efficiency decrease as function of the ammonia content is also less pronounced when increasing the intake pressure: from 97% at 0% NH3 down to 94% at 15% NH3. Empty symbols: intake pressure of 1 bar; Filled symbols: intake pressure of 1.2 bar

CONCLUSION

In this work, a syngas composition, representative of sewage sludge feedstock on SI engine performances and exhaust emissions was studied. First, a comparison with pure methane was established and confirmed that the global performances with syngas were decreased in comparison to those with CH4 only. However, increasing the intake pressure (only by 20%) showed that slightly boosted operation allows to reach similar performances. In the other hand, fuelled an engine with syngas exhibits less pollutant emissions than with methane and especially NOx ones. Secondly, syngas was blended with small ammonia amount (maximum 15% volume) to evaluate the impact of this impurity specie, which can be present after the gasification process. The presence of ammonia induces an increase of the energy content in the fuel, which provides higher output produced work. But with this syngas specific composition, the presence of ammonia tends to slow down the global flame speed hence to extend the combustion duration and to slightly decrease the indicated efficiency. The NOx emissions of the 15% ammoniaadded syngas were comparable to the methane whereas HC and CO showed lower values for the ammonia-syngas blend compared to methane. However, for the highest additions of ammonia (10 and 15% vol), the combustion efficiency decreases probably because of the amount of unburned ammonia in the exhaust especially for atmospheric intake pressure.

Thus, ammonia impurities produced through gasification with a N-rich sample might not be as disadvantageous as expected to be used in genset if a catalytic exhaust aftertreatment system is mounted because of the amount of NOx and possible unburned ammonia emissions. The lack of quantification about the exhaust ammonia is also an issue to conclude entirely on the ammonia-added syngas properties and should be further investigated. It could be also interesting to study experimentally the fundamental properties of ammonia syngas blends such as laminar flame speed in order to validate potential kinetic mechanisms.

As a perspective, the usage of ammonia could potentially be beneficial in financial and technological terms since it could save a purification process during the gasification or because blending syngas from waste on one side and ammonia as an e-fuel on the other side could increase the flexibility of the system and prevent from shortage.

Figure 3 :

 3 Figure 3: IMEP (filled symbols) and Indicated efficiency (empty symbols) of syngas compared to methane as a function of the equivalence ratio

Figure 4 :

 4 Figure 4: Net heat release rate of syngas and methane for several equivalence ratios at 1 bar intake pressure

Figure 5 :

 5 Figure 5: Combustion duration analysis comparing methane and syngas as a function of the equivalence ratio at 1 bar of intake pressure

Figure 6 :

 6 Figure 6: Comparison of exhaust emissions for syngas and pure methane for 3 equivalence ratios at 1 bar of intake pressure

Figure 7 :Figure 8 :Figure 9

 789 Figure 7: In-cylinder pressure (a) and HRR evolution as a function of ammonia content (%Vol). in the syngas/ammonia blend at 1.2 bar intake pressure -𝜙=1

Figure 9 :

 9 Figure 9: Optimised SIT as a function of ammonia content in syngas for two intake pressures -ϕ=1

Figure 11 :

 11 Figure 11: Combustion duration analysis of syngas as a function of the ammonia content in the blend for both intake pressures -ϕ=1

Figure 12 indicates

 12 Figure12indicates how the initiation phase strongly depends on the laminar flame speed: the higher the flame speed the shorter the CA10-SIT. The highest flame speed (65.1 cm/s) is reached for pure syngas and the lowest (29.1 cm/s) for the 15%NH3 in blend. Yet, the decrease of the flame speed as function of the ammonia content in the blend is twofold: i) the lower flame speed of ammonia which decreases the flame speed of the blend; ii) because of this lower flame speed, the SIT value must be advanced to keep the best IMEP with the lowest cycle to cycle variations (Figure9), thus leading to different in-cylinder pressure and temperature conditions. During the initial phase, the flame is not yet strongly affected by the turbulence inside the combustion chamber. On the contrary, for the self-sustained propagation phase, i.e. CA50-CA10, the combustion duration is constant as function of the laminar flame speed. Once the combustion is well initiated, i.e. after CA10, the flame-turbulence interaction would enable a better oxidation of the fuel, counterbalancing the impact of ammonia.

Figure 12 :

 12 Figure 12: Combustion duration as function of the laminar flame speed calculated at SIT conditions and for intake pressure of 1 bar -ϕ=1

Figure 13 :

 13 Figure 13: Evolution of syngas emissions as function of ammonia content at 1 bar intake pressure

Figure 14 :

 14 Figure 14: Combustion efficiency of syngas as a function of ammonia content calculated from Eq.4 and 5.

Table I :

 I Engine specifications

	Displaced volume	535.5 cm 3
	Bore	77 mm
	Stroke	115 mm
	Connecting rod length	177 mm
	Compression ratio	11.7
	Intake valve opening	5 CAD
		After Top Dead Center
		(ATDC)
	Exhaust valve closing	5 CAD
		Before Bottom Dead Center
		(BBDC)

Table II :

 II Syngas compositions from Szwaja et al.[START_REF] Szwaja | [END_REF]

		H2	CO	CH4	CO2	N2
	Gas
		%mol	%mol	%mol	%mol	%mol
	Content	13.00	16.00	3.00	15.00	53.00
	The stoichiometric ammonia-added syngas reaction can be expressed as follows:
	𝑎𝐻 + 𝑏𝐶𝑂 + 𝑐𝐶𝐻 + 𝑑𝐶𝑂 + 𝑒𝑁 + + + 2𝑐 + 𝑥 (𝑂 + 3.76𝑁 ) + 𝑥𝑁𝐻 → (𝑎 + 2𝑐 +

𝑥)𝐻 𝑂 + (𝑏 + 𝑐 + 𝑑)𝐶𝑂 + (𝑒 + 3.76 + + 2𝑐 + 𝑥 + )𝑁

Table III :

 III Table III summarizes all experimental conditions of this study and Table IV displays the main characteristics for each blend. Engine test condition, N=1400 rpm, intake temperature Tin=30°C

		Equivalence		Intake
	Fuel	Ratio	% NH3	pressure
		(ϕ)		Pin (bar)
	CH4	0.9;1;1.1	0	1
	Pure
		0.9;1;1.1	0	1;1.2
	syngas
	Syngas		0;2.5;5;
		1		1;1.2
	+ NH3		7.5;10;15

Table IV :

 IV Mixture properties

		Stoichiometric	LHV
	Fuel composition
		air-fuel ratio (AFR st )	(MJ.kg -1 )
	Methane	9.520	50.33
	100% syngas	1.512	3.35
	97.5% syngas
		1.474	3.53
	2.5% NH3
	95% syngas
		1.436	3.71
	5% NH3
	92.5% syngas
		1.399	3.89
	7.5% NH3
	90% syngas
		1.361	4.08
	10% NH3
	85% syngas
		1.285	4.44
	15% NH3
	2.4 Post Processing

 This work was made possible by the financial support of the CNRS/FITe (FR2036): "Fédération pour l'Innovation et la Transition énergétique".  The Authors are grateful to B. Raitiere of Univ. Orléans, PRISME for technical support.