Romain Negrel
email: romain.negrel@ensea.fr

David Picard
email: picard@ensea.fr

Philippe-Henri Gosselin
email: gosselin@ensea.fr

Evaluation of Second-order Visual Features for Land-Use Classification

This paper investigates the use of recent visual features based on second-order statistics, as well as new processing techniques to improve the quality of features. More specifically, we present and evaluate Fisher Vectors (FV), Vectors of Locally Aggregated Descriptors (VLAD), and Vectors of Locally Aggregated Tensors (VLAT). These techniques are combined with several normalization techniques, such as power law normalization and orthogonalisation/whitening of descriptor spaces. Results on the UC Merced land use dataset shows the relevance of these new methods for land-use classification, as well as a significant improvement over Bag-of-Words.

I. INTRODUCTION

In land-use classification of high-resolution overhead imagery, the most popular pipeline is composed of two parts: the extraction of image descriptors; and the use of statistical learning tools. To extract the descriptors, two types of methods are used: global descriptors; aggregated descriptors. The global descriptors commonly used are color descriptors [START_REF] Yang | Bag-of-visual-words and spatial extensions for land-use classification[END_REF] (e.g. histogram of RGB, HSV, CIE Lab) and homogeneous texture descriptors [START_REF] Yang | Bag-of-visual-words and spatial extensions for land-use classification[END_REF] (Gabor filter responses). The aggregated image descriptors are computed in two steps: the extraction of a set of local descriptors; and their aggregation in a single descriptor. The most polular local descriptors is the well known SIFT descriptor [START_REF] Lowe | Object recognition from local scale-invariant features[END_REF]. Many aggregation methods have been proposed and evaluated [START_REF] Yang | Bag-of-visual-words and spatial extensions for land-use classification[END_REF], [START_REF] Yang | Spatial pyramid co-occurrence for image classification[END_REF][START_REF] Risojevic | Orientation difference descriptor for aerial image classification[END_REF][START_REF] Cheriyadat | Unsupervised feature learning for aerial scene classification[END_REF][START_REF] Zhang | High-resolution remote-sensing image classification via an approximate earth mover's distance-based bag-of-features model[END_REF][START_REF] Aptoula | Remote sensing image retrieval with global morphological texture descriptors[END_REF]. Yang et al. [START_REF] Yang | Bag-of-visual-words and spatial extensions for land-use classification[END_REF], [START_REF] Yang | Spatial pyramid co-occurrence for image classification[END_REF] evaluated numerous methods used in computer vision: the bag-of-visualwords [START_REF] Sivic | Video google: A text retrieval approach to object matching in videos[END_REF] and its spacial extentions (Spatial Pyramid Match Kernel [START_REF] Lazebnik | Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories[END_REF] and Spatial Co-occurrence Kernel [START_REF] Haralick | Textural features for image classification[END_REF]). Risojevic et al. [START_REF] Risojevic | Orientation difference descriptor for aerial image classification[END_REF] propose to use the cross-cerrelations between Gabor wavelet coefficient and quaternion framework for the representation of color images to compute the image descriptor. Recently, Cheriyadat et al. [START_REF] Cheriyadat | Unsupervised feature learning for aerial scene classification[END_REF] propose to use a coding/pooling method [START_REF] Wang | Localityconstrained linear coding for image classification[END_REF] to aggregate the local descriptors into a single descriptor.

In this paper, we propose to evaluate recent methods of aggregated descriptors for visual representation of land-use classification in high-resolution overhead imagery [START_REF] Yang | Bag-of-visual-words and spatial extensions for land-use classification[END_REF]. This includes improved Fisher Vectors (FV) [START_REF] Perronnin | Improving the fisher kernel for large-scale image classification[END_REF], Vectors of Locally Aggregated Descriptors (VLAD) [START_REF] Jegou | Aggregating local image descriptors into compact codes[END_REF], Vectors of Locally Aggregated Tensors (VLAT) [START_REF] Picard | Efficient image signatures and similarities using tensor products of local descriptors[END_REF], and many improvements that were recently proposed [START_REF] Negrel | Web scale image retrieval using compact tensor aggregation of visual descriptors[END_REF]. These methods are the extension of visual dictionaries approaches introduced by Bag-of-Words (BoW) [START_REF] Sivic | Video google: A text retrieval approach to object matching in videos[END_REF], thanks to several key ideas. A first key idea is the introduction of the deviation approach, which was first motivated by statistical models from Fisher Kernels [START_REF] Perronnin | Improving the fisher kernel for large-scale image classification[END_REF]. The idea is to consider the deviation between the local image model and a global model, rather than only the local image model. As a result, specific properties of an image are better emphasised. A second key idea is the introduction of secondorder statistics, for instance using covariance data in addition to mean data. While the feature size is then significantly increased, recent techniques for high dimensionality reduction have solved this problem [START_REF] Jegou | Aggregating local image descriptors into compact codes[END_REF], [START_REF] Negrel | Web scale image retrieval using compact tensor aggregation of visual descriptors[END_REF]. Another key idea is the introduction of normalisation processing at the different levels of the tool-chain, like the power law [START_REF] Perronnin | Improving the fisher kernel for large-scale image classification[END_REF] or cluster-wise component analysis [START_REF] Negrel | Web scale image retrieval using compact tensor aggregation of visual descriptors[END_REF]. A last key idea is to only consider features vectors compared with a linear similarity (e.g. dot product). These constraints allow the use of very efficient retrieval and learning techniques, like Stochastic Gradient Descent SVM [START_REF] Bottou | Large-scale machine learning with stochastic gradient descent[END_REF]. Furthermore, in most cases this leads to methods with computational and memory complexity linear with the size of datasets, rather than quadratic complexity with non-linear features.

In this scope, we first propose a detailed presentation of these methods in Section II. Then, we present in Section III evaluation results on UC Merced land use dataset [START_REF] Yang | Bag-of-visual-words and spatial extensions for land-use classification[END_REF].

II. IMAGE FEATURES

Most image features are obtained by a two steps scheme. The first step is to extract a set of local visual descriptors from the images. The most commonly used visual descriptors are highly discriminant local descriptors (HOG, SIFT, SURF, ...). Regions of interest can be selected by uniform sampling, or by automatic point of interest detection. The set extracted from an image is called a bag. We denote by B i = {b ri } r the set of descriptors b ri ∈ R D in image i. The second step is to map the descriptors of the bag B i into a single vector x i ∈ R W , known as the image feature.

A. Statistical Approaches

The first methods to map the descriptors in a feature are based on the statistical study of the distribution of descriptors in the bag. These approaches have been inspired by text retrieval methods. To study the distribution of descriptors, we use a visual codebook composed by C visual words. The visual codebook is generally computed by a clustering algorithm (e.g., k-means) on a large sample of descriptors. A bag can then be described by a statistical analysis of occurrences of visual words.

The first method of this kind, named Bag of Words (BoW) [START_REF] Sivic | Video google: A text retrieval approach to object matching in videos[END_REF] counts the number of descriptors belonging to each cluster. The dimension of the feature is then C. Many extension of BoW has been proposed [START_REF] Wang | Localityconstrained linear coding for image classification[END_REF], for example in the classification of urban scenes in geo-referenced images [START_REF] Iovan | Classification of Urban Scenes from Geo-referenced Images in Urban Street-View Context[END_REF]. These approaches obtain good results in similarity search and in images classification. However, to obtain good results with these methods it is necessary to use visual codebooks with very large dictionaries (about 100k visual word), and the use of a non-linear metric.

B. Model Deviation Approaches

Perronnin et al. [START_REF] Perronnin | Improving the fisher kernel for large-scale image classification[END_REF] proposed a successful method called Fisher Vectors. This method uses a probability density function denoted by u λ of parameters λ as model of the descriptors space. To describe the image, they compute the derivative of the log-likelihood of image descriptors to the model:

G Bi λ = 1 T ∇ λ log u λ (B i ). (1)
The authors propose to use a Gaussian Mixture Model (GMM) of parameters µ c and σ c . Elements of the Fisher Vector for each Gaussian c can be written as:

G Bi µ,c = 1 T √ ω c r γ c (b ri ) b ri -µ c σ c , (2)
G Bi σ,c = 1 T √ ω c r γ c (b ri ) (b ri -µ c ) 2 σ 2 c -1 . (3)
Where (ω c , µ c , σ c ) are the weight, mean and standard deviation of Gaussian c, and γ c (b ri ) the normalized likelihood of b ri to Gaussian c. The final feature is obtained by concatenation of G Bi µ,c and G Bi σ,c for all Gaussians. Fisher Vectors achieve very good results [START_REF] Perronnin | Improving the fisher kernel for large-scale image classification[END_REF]. However, Fisher Vectors are limited to the simple model of mixtures of Gaussians with diagonal covariance matrices. Moreover, the GMM algorithm is computationally very intensive.

Jegou et al. [START_REF] Jegou | Aggregating local image descriptors into compact codes[END_REF] proposed a simplified version of Fisher Vector by aggregating local descriptors, called Vectors of Locally Aggregated Descriptors (VLAD). They proposed to model the descriptors space by a small codebook obtained by clustering a large set of descriptors. The model is simply the sum of all centered descriptors B ci = {b rci } r ⊆ B i from image i and cluster c:

ν ci = brci∈Bci b rci -µ c (4)
with µ c the center of cluster c. The final feature is obtained by a concatenation of ν ci for all c. The feature size is D × C.

Picard et al. [START_REF] Picard | Efficient image signatures and similarities using tensor products of local descriptors[END_REF] proposed an extension of VLAD by aggregating tensor products of local descriptors, called Vector of Locally Aggregated Tensors (VLAT). They proposed to use the covariance matrix of the descriptors of each cluster. Let us denote by "µ c " the mean of cluster c and "T c " the covariance matrix of cluster c with b rci descriptors belonging to cluster c:

µ c = 1 |c| i r b rci (5)
T c = 1 |c| i r (b rci -µ c )(b rci -µ c ) ⊤ , (6)
with |c| being the total number of descriptors in cluster c.

For each cluster c, the feature of image i is the sum of centered tensors of centered descriptors belonging to cluster c:

T ic = r (b rci -µ c )(b rci -µ c ) ⊤ -T c . (7)
Each T ic is flattened into a vector v ic . The VLAT feature v i for image i consists of the concatenation of v ic for all clusters:

v i = (v i1 . . . v iC ). (8)
As the T ic matrices are symmetric, only the diagonal and the upper part are kept while flattening T ic into a vector v ic . The size of the feature is then C × D×(D+1) 2 .

C. Normalization

Several normalization processing are proposed in the literature to enhance the quality of visual features. The most popular one is the power law normalization, which first raises each value to a power α, and then ℓ 2 -normalize the resulting vector:

x i = v ′ i v ′ i , ∀j, v ′ i [j] = sign v i [j] v i [j] α , (9)
with α typically set to 0,5. This normalization was first introduced for the final visual features [START_REF] Perronnin | Improving the fisher kernel for large-scale image classification[END_REF], and more recently for the normalization of low-level descriptors, such as Root-SIFT [START_REF] Arandjelovic | Three things everyone should know to improve object retrieval[END_REF].

Another common improvement is the orthogonalization and/or whitening of vector spaces, using a Principal Component Analysis. This can be performed at different levels. For instance, this is a required pre-processing on low-level descriptors for Fisher Vectors [START_REF] Perronnin | Improving the fisher kernel for large-scale image classification[END_REF]. It can also be used to normalize each cluster of the dictionary, as it is done for VLAD [START_REF] Delhumeau | Revisiting the vlad image representation[END_REF] and for VLAT [START_REF] Negrel | Web scale image retrieval using compact tensor aggregation of visual descriptors[END_REF].

In the case of VLAT, the processing is the following one. First, we compute the eigendecomposition of the covariance matrix of each cluster c:

T c = V c D c V ⊤ c , (10)
where D c is a real non-negative diagonal matrix (eigenvalues), and V c is unitary (eigenvectors). Then we project the centered descriptors belonging to c on the eigenvectors:

b ′ rci = V ⊤ c (b rci -µ c ). (11)
Combining eq.( 11) and eq.( 7), we get:

T ic = V ⊤ c r (b rci -µ c )(b rci -µ c ) ⊤ -T c V c = r V ⊤ c (b rci -µ c )(b rci -µ c ) ⊤ V c -D c = r V ⊤ c (b rci -µ c ) V ⊤ c (b rci -µ c ) ⊤ -D c .
The cluster-wise normalized VLAT feature of image i in cluster c is the sum of tensors of projected descriptors b ′ rci belonging to cluster c, centered by D c :

T ic = r b ′ rci b ′⊤ rci -D c . ( 12
)
Agricultural Airplane Base Ball D.

III. EXPERIMENTS

In this section, we present the result using FV, VLAD and VLAT features on UC Merced land use dataset [START_REF] Yang | Bag-of-visual-words and spatial extensions for land-use classification[END_REF].

A. Dataset

This dataset is composed of 256 × 256 pixels RGB images, with pixel resolution of one foot. They are manually classified into 21 classes, corresponding to various land cover and land use types: agricultural, airplane, baseball diamond, beach, buildings, chaparral, dense residential, forest, freeway, golf course, harbor, intersection, medium density residential, mobile home park, overpass, parking lot, river, runway, sparse residential, storage tanks, and tennis courts. Each class contains 100 images. Examples are presented in Fig. 1.

We evaluate methods using the same protocol as in [START_REF] Yang | Bag-of-visual-words and spatial extensions for land-use classification[END_REF]. This is a five-fold multi-classification protocol. We randomly split the dataset into five subsets, train using four subsets and test using the remaining one. The results presented in the following sections are then average classification accuracy over the five runs. We follow a one-versus-all classification strategy: for each class, we train a linear SVM classifier, and label each test image with the classifier that returns the highest classification score. For all the following experiments, we ℓ 2 normalize lowlevel descriptors (no power law at this stage), and normalize final visual features using a power law with α = 0.5.

B. Features comparison

We first evaluated the scale parameter of low-level descriptors. For this purpose, we used HOG descriptors at different scales: cells of 4 pixels, 6 pixels, 8 pixels and 10 pixels. Then, we carried out experiment with VLAD features for different dictionary sizes D, from 32 to 256 keywords. As presented in Table I, individual scales presents similar performance. Furthermore, this behaviour is the same with different dictionary size: in all cases, performance is improved by the size of the dictionary, but is similar from one scale to another. However, when combining all scales, the performance is improved. As a result, we always consider the combination of these four scales in the following experiments.

The second set of experiments compares FV, VLAD and VLAT using HOG descriptors and RGB descriptors [START_REF] Perronnin | Improving the fisher kernel for large-scale image classification[END_REF], for different dictionary sizes D. Results are presented in Table II. If we compare HOG and RGB descriptors, HOG are more effective. Let note that we did not combine HOG and RGB descriptors because no normalization is performed on descriptor spaces in these experiments. Focusing on the dictionary size, improvement can be observed with VLAD, however, this is less significant for FV and VLAT. Finally, the best feature in these experiment is the VLAT feature.

C. Descriptor spaces normalization

In this section, we analyse the normalization of descriptor spaces using methods based on Principal Components Analysis (PCA). The motivation is two-fold: improve the quality of features, but also combine several features from different descriptors. For FV, we considered different number d of component for the normalization and reduction of low-level descriptors. For VLAD and VLAT, this processing is performed for each cluster of the visual dictionary. Results are presented in Table III, were d is the number of principal component for the global PCA (FV) or for cluster-wise PCAs (VLAD and VLAT). Note that cases with 128 components are not presented for RGB descriptors, since the original descriptor only have 96 dimensions. Furthermore, the size of the dictionary is always set of 64.

Overall, if we compare to the previous set of experiments with D = 64, performance is increased in all cases, even if we reduce the size of descriptors to d = 32 dimensions. If we compare HOG and RGB descriptors, HOG are also more effective. Thanks to the normalization, we can create a relevant combination of HOG and RGB descriptors, which present the highest results, especially with VLAT features.

We present in Fig. 2 these results according to the size of features, using HOG and RGB descriptors, and dictionary sizes D = 32 and D = 64. If we focus on features with 1,000 dimensions, we can see that performance over 92% can be obtained with FV and VLAD methods. This can be compared to the results using BoW [START_REF] Yang | Bag-of-visual-words and spatial extensions for land-use classification[END_REF], which are from 77% to 82% with the same number of dimensions. Finally, we compare our results to the state of the art in Table IV. As we can see, model deviation approaches clearly outperform their methods by a fair margin of at least 9%.

IV. CONCLUSION

In this paper, we presented recent methods for computing visual features, as well as related normalization techniques. We carried out experiments to analyse the benefit of each new component on the UC Merced land use dataset [START_REF] Yang | Bag-of-visual-words and spatial extensions for land-use classification[END_REF]. Overall, all considered method (FV, VLAD and VLAT) showed high classification accuracies. Furthermore, a high accuracy can be obtain with small visual dictionaries (64 words), especially for FV and VLAT features. Descriptor space normalization using PCA-based methods improve the results, but also allows effective combination of different descriptors types (HOG and RGB in our experiments). Finally, when compared to BoW methods, these methods lead to large performance improvement, as it is seen for generalist image categorization. A new component that will be worth investigating in the future is the compression of visual features for large-scale indexing, and their evaluation on very large land-use datasets.

Fig. 1 .

 1 Fig. 1. UC Merced land use dataset.

Fig. 2 .

 2 Fig. 2. Classification accuracy (%) as function of dimensionality reduction of descriptors (i.e., d ∈ {96, 64, 32}) for various visual codebooks size and features.

TABLE I .

 I CLASSIFICATION ACCURACY (%) FOR VARIOUS EXTRACTION SCALES OF HOG DESCRIPTORS AND VARIOUS VISUALCODEBOOKS SIZE WITH VLAD FEATURE.

	D	32	64	128	256
	4	84.5	86.7	88.5	89.0
	6	84.8	86.0	87.6	89.0
	8	83.2	86.7	87.7	88.6
	10	83.8	86.5	87.8	88.7
	4+6+8+10	85.2	87.5	89.6	90.9

TABLE IV .

 IV COMPARISON TO STATE-OF-THE-ART RESULTS.

	Method	BOVW[1]	SCK[1]	BOVW+SCK[1]	ColorRGB[1]	ColorHLS[1]	Texture[1]	Our (VLAT)
	Accuracy (%)	76.81	72.52	77.71	76.71	81.19	76.91	94.3
	Method	SPCK[3]	SPCK+[3]	SPCK++[3]	Color O.F.[4]	Color O.D.[4]	Quater. O.D.[4]	Our (FV)
	Accuracy (%)	73.14	76.05	77.38	81.10	84.86	85.48	93.8

TABLE III .

 III CLASSIFICATION ACCURACY (%) FOR HOG AND RGB DESCRIPTORS AND FOR VARIOUS COMPRESSION RATIOS OF DESCRIPTORS WITH FV, VLAD, VLAT FEATURES (D = 64).

				d
			128	96	64	32
		FV	92.0	91.5	91.2	90.1
	HOG	VLAD	89.1	88.8	88.9	87.4
		VLAT	92.5	92.7	92.7	91.8
		FV	-	89.3	89.4	87.2
	RGB	VLAD	-	86.5	86.0	84.6
		VLAT	-	90.6	90.5	89.4
		FV	-	93.8	93.4	92.8
	HOG+RGB	VLAD	-	92.5	92.5	91.6
		VLAT	-	94.1	94.3	93.8


Maria Strantza
email: maria.strantza@vub.ac.be

Dieter De Baere

Marleen Rombouts

Stijn Clijsters

Isabelle Vandendael

Herman Terryn

Patrick Guillaume

Danny Van Hemelrijck

3D Printing for Intelligent Metallic Structures

Keywords: structural health monitoring, pressure, additive manufacturing, fatigue, smart structure

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

For aerospace, civil and mechanical engineering structures implementing damage detection is mandatory to improve life-safety and to reduce the direct operational costs. This process is referred to as structural health monitoring (SHM) [START_REF] Farrar | An introduction to structural health monitoring[END_REF]. The maturity of the different SHM systems is expressed in technological readiness levels (TRL) by the industry (e.g. Airbus) and governmental research organisation (e.g. NASA). The definition can vary for the different organisations but the main idea remains similar. A detailed review of the different TRL levels can be found elsewhere [START_REF] Roach | Does the maturity of Structural Health Monitoring technology match user readiness?[END_REF].

The research of effective SHM systems is resulting from the need for life-safety and the reduction of the direct operational costs. A new concept, the so-called effective structural health monitoring (eSHM) system has the intrinsic capability to accomplish the specified requirements but still needs to pass the different TRL levels to enable an implementation. The eSHM system is produced by 3D printing or additive manufacturing. Additive Manufacturing (AM) is a "process of joining materials to make objects from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing methodologies", as defined by ASTM International F42 committee on Additive Manufacturing Technologies [START_REF] Astm Standard | Standard Terminology for Additive Manufacturing Technologies[END_REF]. Different AM technologies exist for the production of the materials [START_REF] Gibson | [END_REF]. They differ in the way layers are built to create parts, and the materials that can be used. Some methods are using melting to create the layers, whereas others methods are laying liquid materials that are cured with different technologies [START_REF] Levy | Rapid manufacturing and rapid tooling with Layer Manufacturing (LM) technologies, state of the art and future perspectives[END_REF]. For the production of metal parts there are also a lot of technologies like Selective Laser Melting (SLM) [START_REF] Kruth | Selective laser melting of iron-based powder[END_REF] and Laser Metal Deposition (LMD) [START_REF] Sexton | Laser cladding of aerospace materials[END_REF]. Both AM processes use the energy of a laser beam to bind powder particles to each other in a layer-wise manner providing full melting.

Fatigue is one of the damage phenomena that can affect the structural integrity of a metallic structural component. Microcracks typically initiate at the outer surface of the material, also in unnotched specimens with a nominally homogeneous stress distribution tested under cyclic tension. The restrain on cyclic slip is lower at the outer surface compared to the inside of the material because of the free surface at one side of the surface material. It is concluded that fatigue crack initiation is a surface phenomenon [START_REF] Schijve | Fatigue of structures and materials in the 20th century and the state of the art[END_REF]. In this study, the step method was deemed a suitable method and it can provide an endurance stress for particular stress ratio with a single specimen as well as a method of generating endurance limits for one-of-a-kind specimens [START_REF] Bellows | Validation of the step test method for generating Haigh diagrams for Ti-6Al-4V[END_REF].

The objective of this study is to demonstrate the scientific feasibility (TRL 3) of the SHM concept for samples produced by LMD and SLM under laboratory controlled conditions. During this study the analytical and experimental functioning will be verified and the scientific feasibility will be fully demonstrated as TRL3 requires. This implies to prove that fatigue cracks can be detected with the capillary network and to indicate that the integrated SHM system has no significant impact on the crack initiation behaviour and location.

EFFECTIVE STRUCTURAL HEALTH MONITORING APPROACH

The basic physical principle of the eSHM system is checking the absolute fluid pressure changes in a 3D-curved network of capillaries or cavities that are integrated with additive manufacturing techniques in the interior of a part made with AM techniques or integrated on a standard machined part. A pressure change in the capillary indicates the presence of a crack. The 3D schematic representation of the four point bending test specimens is presented in Figure 2. At first sight, this it is a very straightforward and robust approach to monitor the appearance of a crack; the measurement principle has already been used in the past for straight elongated capillaries [START_REF] Wanhill | Significance of a rotor blade failure for fleet operation, inspection, maintenance, design and certification[END_REF]. A first asset of this damage identification approach is that the inspection of a complete zone can be done with one sensor at a single point on the component. A second asset is that the damage identification can be done with sensors that are available off the shelf and have already proven industrial robustness.

The practical implementation of this eSHM system, which requires the integration of internal curved capillaries/cavities in the structure, is challenging from a production as well as from a structural integrity point of view. Laser-based AM technologies like SLM and LMD are promising technologies that show potential to implement eSHM systems for fatigue crack detection. In this paper, stainless steel AISI 316L and Ti6Al4V parts with integrated capillaries are manufactured using respectively the LMD and SLM technologies. The fatigue behaviour of both materials is investigated with a four-bending test, with special attention for the crack initiation and detection. The latter are investigated through analysis of the fracture surfaces with a scanning electron microscope. During the fatigue tests, the specimens are subjected to the step method with constant fatigue stress amplitude and a constant R ratio in each step.

EXPERIMENTAL STUDY

Materials and Production

Since one of the main scientific objectives of the test was to indicate that the capillary had no significant impact on the fatigue initiation behaviour of the test specimen, a 3D sinusoidal shape of the capillary was added to support this characteristic. The integrated capillary for the LMD SS316L samples has a diameter of 3mm, sinusoidal shape with amplitude of 2mm and a period of 25.7mm. On the other hand, the integrated capillary for the SLM Ti6Al4V samples has a diameter of 1mm, sinusoidal shape with amplitude of 2mm and a period of 25mm. After LMD and SLM the specimens were milled to final dimensions as specified in Figure 2. The static tensile properties for the LMD AISI 316L material and SLM Ti6Al4V are presented in Table 1 and2

Test Procedure

The specimens were subjected to a cyclic fatigue loading with constant amplitude. A schematic of the four-point bending fatigue test is depicted in Figure 3. The locations with the highest probability for crack initiation from the capillary are situated at those cross-sections of the capillary that are furthest away from the neutral axis. The principal reason for this higher probability is directly related to the higher stress concentration at these locations. According to the step method the initial stress amplitude is chosen below the fatigue limit of the specimen. For each step a large number of cycles N with the same loading are applied. If failure did not occur the stress amplitude is increased with a selected step and again N numbers of cycles are applied [START_REF] Sexton | Laser cladding of aerospace materials[END_REF]. This procedure is repeated until failure occurs. Since no previous results were available for the endurance limit of the additively manufactured AISI 316L and Ti6Al4V studied in this paper a conservative stress starting point was chosen. The fatigue tests were carried out on an MTS machine with a maximum static and dynamic load capability of 100kN. The applied sinusoidal cyclic load in each step had constant amplitude with an R ratio equal to 0.1 and frequency of 5Hz for SS316L (LMD) and 15Hz for Ti6Al4V (SLM). During the tests the specimen pressure transducer, the actuator load and displacement were monitored and registered by the controller of the MTS machine.

The specimens were equipped with a pressure transducer (Kulite XTL-123C-190M-1, 7-BAR-A) at one side and with a check valve (Clippard MCV-1-M5) at the other side. After sealing, each specimen was placed in a vacuum chamber until the air in the capillary reached an absolute pressure of 0.55 bar. As a final step an extra stop (Clippard 11755-M5-PKG) was installed on the check valve as an extra occlusion. The final test specimen assembly is shown in Figure 4. In order to analyse the influence of the structural health monitoring on the micro-crack initiation, the fracture surfaces obtained after the fatigue tests were analysed with a scanning electrode microscope.

RESULTS

Effective structural health monitoring during fatigue test

The first specimen of LMD AISI 316L has been subjected to nine steps, which corresponds to a total of 1.309.009 loading cycles for the first specimen before the crack penetrated the capillary and the test was automatically stopped. For each step 100.000 cycles were tested with the exception of step 8 and 9 where respectively 500.000 and 109.009 cycles were applied. The peak stress started from 9.9% of σ UTS (56MPa) and reached 68.9% of σ UTS (391MPa) in step 9 (see Table 3).

For the last specimen of LMD AISI 316L, in total 983.185 cycles within 4 steps were applied. The starting stress level was set at 79% of σ UTS (446MPa) with 100.000 cycles per step. At the stress level of 89% of σ UTS (502MPa) the number of cycles was increased to 500.000 cycles. The failure occurred according to the eSHM system after 283.185 cycles at 93.5% of σ UTS (530MPa) in Step 4 (Table 3).

For the initial step of the first specimen of SLM Ti6Al4V the cyclic peak stress was limited to 25 % of σ y and the number of cycles per step to 500.000. In subsequent steps the peak stress was increased by 10% of σ y until the fatigue load reached the value of 71% of σ y , where the crack was detected by the eSHM system. Twelve steps were tested for the first specimen. This gave a total of 5.514.591 loading cycles before the crack reached the capillary and the test was automatically stopped. For each step 500.000 cycles were tested with the exception of step 12 where 14.591 cycles were applied until the failure. The peak stress started from the 21,5 % of σ UTS (207 MPa) and reached 69 % of σ UTS (590 MPa) in step 12 (Table 3).

Regarding the last specimen of SLM Ti6Al4V the peak stress was increased by 10% of σ y until the fatigue load reached the value of 69% of σ y , where the crack was detected by the eSHM system. Six steps were tested, this gave a total of 2.752.000 loading cycles before a crack between the capillary and the outer surface of the part is formed. For each step 500.000 cycles were tested with the exception of step 6 where 252.000 cycles were applied until the failure, reaching 69 % of σ UTS (570 MPa-Table 3). During the tests at specified intervals and specifically at the last 10 completed loading cycles the absolute pressure level has been registered before the final failure. The absolute pressure increases stepwise when the crack penetrates the capillary. The varying character of the pressure increase is linked with the cyclic loading and the fact that the crack opens and closes during each load cycle. This phenomenon could be clearly observed by plotting the pressure values vs elapsed time for the last applied load cycles (see Figure 5(a), (b), (c) and(d)). For all the specimens the preset limit was 0.75 bar except the second specimen of LMD AISI 316L, where the pre-set limit was set to be 0.65 bar. On the moment that the pressure exceeded this value the actuator was switched off automatically. It is worth to mention that there is a difference in pressure behaviour between graphs (a), (b) and (d) with the graph (c) of the specimens. Currently, there is no clear explanation on this difference and further investigation needs to be done.

Fractographic analysis of the specimens after fatigue tests

According the ASM handbook of Fractography, a fracture surface is a detailed record of the failure history of the part. It contains evidence of loading history, environmental effects, and material quality [START_REF] Mills | ASM Handbook[END_REF]. Fracture analysis confirmed for the first specimen of LMD AISI 316L that the crack initiated from the outer surface towards the capillary (Figure 6 (a)). At the crack initiation locations oxide inclusions were detected on the fracture surface (Figure 6(a)). SEM-EDX analysis indicated that the oxide was rich in silicon, chromium and manganese. Fractography shows also that no cracks have initiated at the capillary (Figure 6(a)). Alam et al. [START_REF] Alam | Surface pore initiated fatigue failure in laser clad components[END_REF] demonstrated that close-to-surface pores and oxide inclusions are critical stress raisers and can initiate fatigue cracking having a severe effect on the fatigue life of structural components. For the first specimens of SLM Ti6Al4V (Figure 6(b)) the fracture surface is quite faceted and does not exhibit fatigue striations. The fractographic pictures reveal that the crack has initiated at the capillary region. A close-up of the initiation area is showing high concentrated porosity next to the capillary region (Figure 6(b)). Pores in the interior of the part could be observed on the fracture surface but they did not affect the crack initiation or propagation.

It can be concluded that high local porosity is an essential parameter for the effectiveness of the eSHM system. Fracture analysis has proven that the capillary has no impact on the crack initiation. However porosity is the main reason for crack initiation. It is of high importance to mention that the fracture surface could not be analysed on the second SLM Ti6Al4V specimen and the second LMD AISI 316L specimen. This is due to the fact that the eSHM system detected the crack but the location of the crack was not clear. As a result, further investigation of these samples is still needed.

CONCLUSION

All the tests were successfully stopped by the eSHM system that indicated the presence of a crack. During fatigue, the crack initiates due to porosity or oxide inclusions. This is evidence that concentrated porosity on additive manufacturing materials is the most essential parameter for crack initiation and further investigation is required. Fracture analysis is proving that the integrated SHM system had no influence on the crack initiation location for this four-point bending test setup and specimen. It can be concluded that the study and the experiments constitute the "proof-of-concept" validation of this application. As a result, the objective of this study, reaching the technological readiness level 3, has been proven.

Figure 1 :

 1 Figure 1: 3D schematic representation of eSHM system with integrated sinusoidal hollow channel

  respectively.

Figure 2 :

 2 Figure 2: Four-point bending specimen's dimensions (mm): 2D side and front views

Figure 3 :

 3 Figure 3: Schematic four-point bending setup

Figure 4 :

 4 Figure 4: Final test specimen

Figure 5 :

 5 Pressure vs elapsed time for (a) the first AISI316L (LMD), (b) the second AISI 316L, (c) the first SLM Ti6Al4V and (d) the second SLM Ti6Al4V samples during the last load cycles

Figure 6 :

 6 Scanning electron microscope image of the fracture surfaces of the (a) LMD AISI 316L 1 and (b) SLM Ti6Al4V 1 specimens after the fatigue tests

Table 1 :

 1 LMD AISI 316L mechanical properties

	σ yield (MPa)	σ UTS (MPa)	ε (%)	E tension (GPa)
	280 ± 10	567 ± 13	62 ± 5	163 ± 30

Table 2 :

 2 SLM Ti6Al4V mechanical properties

	σ yield (MPa)	σ UTS (MPa)	ε (%)	E tension (GPa)
	826 ± 17.2	964	12.84	107.71 ± 1.47

Table 3 :

 3 Overview of the different steps and stress levels for the LMD and SLM specimens

	Specimen	Total Steps	Stress Level at	Cycles at last
			Failure	step
	LMD AISI 316L 1	9	391 MPa	109.009
	LMD AISI 316L 2	4	530 MPa	283.185
	SLM Ti6Al4V 1	12	590 MPa	14.591
	SLM Ti6Al4V 2	6	570 MPa	252.000

EWSHM 2014 -Nantes, France

ACKNOWLEDGEMENT

Research funded by an SBO Project grant 110070: eSHM with AM of the Agency for Innovation by Science and Technology (IWT).


M Kharrat

E Ramasso

V Placet

L Boubakar

A SIGNAL PROCESSING METHOD FOR HITS DETECTION AND SEPARATION IN HIGH AE ACTIVITY SYSTEMS: APPLICATION TO COMPOSITE MATERIALS UNDER FATIGUE TESTS

Keywords: Continuous AE signals, Carbon Fiber Reinforced Plastics, Fatigue tests, Wavelet denoising, Hit detection and determination

Acoustic Emission (AE) signals can be classified into three types of transients: bursts, continuous and mixed. Continuous signals consist of multiple overlapping transients emitted from different emission sources among which noise could be found. This noise might sometimes bury relevant information about the integrity of the monitored structures. The threshold-based technique, employed by most of the commercial parameter-based AE systems for hit detection, uses a defined threshold in order to detect the start and the end of the hits. When dealing with continuous emission, the threshold-based technique is not suitable as the burst never drop below the threshold. In this case, the AE system is obliged to force the end of the hit after a defined maximum duration. This issue is encountered in complex systems such as in Organic Matrix Composites (OMC) fatigue tests where a high AE activity is observed, especially when the loading frequency overpasses few hertz. In this case, continuous signals can be generated by the background noise and rubbing, as well as by both cumulated damage (friction of crack surfaces) and damage growth. This paper deals with continuous AE signals obtained from experimental tests carried on Carbon Fiber Reinforced Plastics specimens. A numerical routine was implemented allowing the treatment of these signals. As the size of each acquisition is large due to the sampling rate (generally from 2 to 5 MS/s), the signal was divided into short segments. The Discrete Wavelet Transform (DWT) was used for signal denoising after adapting a certain number of parameters. Hit detection and determination was thereafter performed in order to localize potential hits contained in each signal segment. Conventional AE features were then calculated. By comparing the obtained results to those of conventional threshold-based techniques, we remark that the problem of erroneous hits is overcome. AE information that was hidden by the effect of the noise is now revealed allowing a further interpretation of damage mechanisms in the composite.

INTRODUCTION

Organic Matrix Composites (OMC) and especially Carbon Fiber Reinforced Plastics (CFRP) have seen an increased use in several fields such as aerospace, automotive and civil engineering, due to their high material properties. In numerous applications, metals are being replaced by CFRP composites, whose mechanical properties are increasingly improved (high strength-to-weight ratio, corrosion and heat resistance ...). Acoustic Emission (AE) technique has been widely used during these recent decades for inspection of CFRP composite structures due to its efficiency to detect and localize damages [START_REF] Berthelot | Acoustic emission in carbon fibre composites[END_REF][START_REF] Unnthorsson | Acoustic emission based fatigue failure criterion for cfrp[END_REF][START_REF] Sause | Simulation of acoustic emission in planar carbon fiber reinforced plastic specimens[END_REF]. AE is dependent on some basic deformation and damage mechanisms. In CFRP composites, major damage mechanisms are delamination, matrix cracking, debonding, fiber cracking, and fiber pull-out [START_REF] Gay | Matériaux composites[END_REF].

Acoustic Emission signals can be classified into three types of transients: bursts, continuous and mixed [START_REF] Scruby | An introduction to acoustic emission[END_REF][START_REF] Holroyd | The Acoustic Emission & Ultrasonic Monitoring Handbook. Machine & systems condition monitoring series[END_REF]. Bursts are generally short time-signals induced by the emergence of defects according to one or more of the damage modes. Continuous signals consist of multiple overlapping transients emitted from different emission sources among which noise could be found. Mixed transients include both bursts and continuous signals and are generated by both accrued damage (friction of crack surfaces) and damage growth and, in many cases, superimposed with ambient noise and rubbing [START_REF] Mouritz | Non-destructive evaluation of damage accumulation[END_REF]. This kind of transients is frequently encountered in CFRP composites under fatigue testing during which the specimen can be simultaneously submitted to various solicitations (tension, compression, torsion, and shear) [START_REF] Harris | Fatigue in composites: science and technology of the fatigue response of fibre-reinforced plastics[END_REF]. In addition, the composite inhomogeneity resulting from the difference in material properties of the fibers and matrices will engenders an anisotropy in the velocity of the propagating waves [START_REF] Lowe | Properties of guided waves in composite plates, and implications for nde[END_REF].

In fatigue tests, most of the used machines (uni-axial or multi-axial loading) are working with hydraulic energy to apply desired motions. Such tests can last a long time (quasi-static loadings), which makes the hydraulic fluid hotter. In this environment, a lot of noise is often generated. In fact, uneven flow characteristics and pressure waves are created and transmitted through the fluid. This is known as fluidborne noise whose the pressure wave fluctuations create in turn corresponding force fluctuations. Consequently, this results in a vibration also known as structureborne noise, which is transmitted through the structure [START_REF]Noise Control in Hydraulic Systems[END_REF]. The AE signals received by the distributed sensors are affected by this noise. However, most of the commercial parameter-based AE systems employ the conventional technique based on both threshold and timing parameters for hit detection. A so-called 'Maximum Duration' of each detected hit is defined in the system configuration in order to stop recording of long signals. When dealing with continuous emission, the threshold is permanently exceeded, so the AE signal is recorded entirely as the burst never drop below this threshold. Thus, the conventional AE technique is not suitable, as it is, when dealing with continuous signals.

One of the powerful methods of signal denoising is Wavelet Transform (WT) [START_REF] Baleanu | Advances in wavelet theory and their applications in engineering, physics and technology[END_REF]. Among the applications of the WT theory is the Wavelet denoise method. The WT has been used in many studies [START_REF] Abbate | Application of wavelet transform signal processor to ultrasound[END_REF][START_REF] Kim | Damage detection of structures by wavelet analysis[END_REF] related to the Structural Health Monitoring field dealing with flaw-detection problems. The Wavelet denoise method has shown a good signal-to-noise ratio improvement much better than obtained through some designed filters, and an important ability in processing signals for detecting multiple fault signatures [START_REF] Lin | Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis[END_REF][START_REF] Wang | Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform[END_REF]. Particularly, some studies have reported on the AE signal denoising based on the WT. Feng Y. et al. [START_REF] Feng | Discrete wavelet-based thresholding study on acoustic emission signals to detect bearing defect on a rotating machine[END_REF] have studied the denoising problem of AE signal to detect bearing defect on a rotating machine by using Discrete Wavelet Transform thresholding methods. Satour A. et al. [START_REF] Satour | Acoustic emission signal denoising to improve damage analysis in glass fibre-reinforced composites[END_REF] have developed a continuous wavelet denoising technique and applied it on AE signals obtained from cross-ply composite specimens.

This work deals with continuous acoustic emission in an in-service like environment. For that purpose, a signal processing method is developed for the purpose of conditioning continuous signals caused mainly by ambient noise encountered in fatigue test machines. The time signals are streamed during the test for each sensor. The implemented method uses the wavelet transform to denoise the affected signals after determining optimal denoising parameters that suits with our working conditions. Once the entire signal is denoised, the hit determination is carried out using the conventional threshold method. Practically, a Pencil Lead Break (PLB), as a Hsu-Nielsen source, is performed on the surface of the composite under low and high noise levels. Indeed, a PLB is an aid to simulate an acoustic emission event using the fracture of a brittle graphite lead. This break generates an intense acoustic signal, quite similar to a natural AE source, that the sensors detect as a strong burst [START_REF] Hsu | Characterization and calibration of acoustic emission sensors[END_REF]. The effectiveness of the implemented method is assessed thereafter by comparing some determined features to those obtained by a commercial AE system.

In this paper, the first section gives an overview of the adopted methodology and the used processing techniques. In the second section, the experimental study is addressed. It includes the manipulations performed on a composite ring using a PLB under different noise levels.

AE DATA PROCESSING

The signal processing method

The proposed method is schematized in Figure 1. It consists in several steps in which the continuous emission signal is post-treated after being recorded continuously with a sampling rate of 2 MS/s. Firstly, the signal is loaded. Two possible ways are considered: either the entire signal is processed one shot, or it is partitioned into equal time segments. The second way is adopted when dealing with massive data signals due to a high sampling rate and a long acquisition time. The signal is then denoised using the Wavelet Transform. Afterward, the signal or segment is swept in order to determine potential hits. Using the separated hits, AE features are thereafter computed and stored.

Wavelet signal denoising

The typical procedure of signal denoising using the wavelet theory involves three steps. This procedure is used in this work and it includes:

1. Decomposition: after selecting a wavelet, the signal is decomposed by the wavelet transform at a chosen decomposition level N. 2. Thresholding: after obtaining the detail coefficients, a thresholding is applied to these signal details for each level from 1 to N. Various threshold selection rules exist (fixed form, Stein's Unbiased Risk Estimate principle...) and either a soft or hard thresholding can be applied to the signal [START_REF] Donoho | Ideal spatial adaptation by wavelet shrinkage[END_REF][START_REF] Donoho | De-noising by soft-thresholding[END_REF]. A basic model of the noise has to be taken into account for the thresholding strategy. 3. Reconstruction: computing the reconstructed signal using the original approximation coefficients of level N and the modified detail coefficients of levels from 1 to N.

Hit determination

The hits detection technique used by most of the commercial parameter-based AE systems involves comparing the signal to a defined threshold. This latter is typically set just above the noise and is maintained fixed during the test, or sometimes floating within a defined interval under conditions of high and varying background noise. If the signal surpasses the threshold, a hit is detected and this instant is retained. After detecting the hit, the timing parameters (PDT, HDT and HLT) are usually used in the conventional method in order to determine the hit, i.e. isolate and separate it from the acquired waveform. Once the hit has been determined, AE hit based features can be calculated. Conventional features include Amplitude, Duration, Energy, Counts, Counts-to-peak and Rise time [START_REF] Grosse | Acoustic emission testing[END_REF]. Moreover, some frequency features exist such as Average Frequency, Frequency Centroid and Peak Frequency. In this section, two test configurations are performed using simple experimental AE signals with different noise levels created by the hydraulic system of a tensile machine. This setup is designed in order to simulate an in-service like condition. In both configurations, a simple AE burst is generated using a PLB on the outward surface of an intact specimen. The first configuration is performed under a low noise level, while the second one is done under a high noise level. The test specimen is a 1.5 mm thick CFRP composite ring with an outer diameter of 124 mm and a width of about 16 mm. It is mounted on a tensile testing machine using two clamping jaws (two half-cylinders) as illustrated in Figure 2. These two jaws are not in contact, so the wave propagation is guided only by the composite ring. Four wide-band AE sensors ('Micro80' -Mistras Group Ltd.) with an operating frequency range of [200 -900] kHz and a resonant frequency of 325 kHz are employed. They are equidistributed and fixed directly on the jaws in this manner: Sensors 1 and 4 are on the upper half-cylinder, whereas sensors 2 and 3 are on the lower half-cylinder. It should be mentioned that the hydraulic system is located at the bottom of the machine and is in direct contact with the lower half-cylinder. Consequently, sensors 2 and 3 are intended to be more affected by the generated noise. Table 1 regroups the major AE system settings.

EXPERIMENTAL APPLICATIONS

A PLB under a low noise level

The first configuration consists in performing a PLB on the outward surface of the composite near the sensor N • 1. This PLB is done just after the machine is started, so that the hydraulic system is not yet producing a lot of fluidborne noise. This test is considered as having a low noise level. Figure 3 shows the Duration-Amplitude graphs of the hits detected by the AE system for the four channels. First of all, after a verification of the waveforms obtained from this test, it should be mentioned that the PLB hit is the point having the maximum amplitude in the graphs. In the case of channels 1 and 4, some hits other than those corresponding to the PLB are detected. However, channels 2 and 3 detect a lot of hits with relatively high durations and low amplitudes. This is due to the ambient noise that overpasses the threshold and so launches the hit detection. For the purpose of increasing the signal-to-noise ratio, the affected channels' signals can be denoised and processed by the implemented algorithm. Each channel's signal is loaded and a denoising is performed using a Daubechies wavelet. Consequently, the noise level is greatly reduced under the threshold and the number of the corresponding detected hits is decreased as we can see in Figure 4.

A PLB under a high noise level

In the second configuration, another PLB is created but well after the first test in the same place as the first configuration, so that the fluid in the hydraulic system reaches a high fluctuations' activity.

A higher noise level is then generated. Figure 5 shows the Duration-Amplitude graph of the hits detected by the AE system for all channels. All the detected hits are accumulated at a duration of 200 ms, which corresponds to the predefined maximum duration. Indeed, as we can see in Figure 6, the recorded signals are so noisy (especially for channels 2 and 3) that the amplitude never drops below the threshold throughout all the signal period. Information from channels 2 and 3 is totally hidden meaning that the obtained result in terms of AE features can be likely erroneous. The AE system hence stores segments of 200 ms-long and considers them as detected hits. These latter are considered to be poorly separated since they do not reflect what was really happening in the material.

A further signal processing is then necessary in order to eliminate noise components before performing a hit determination. Figure 6 shows a comparison of the raw and denoised signals of the four channels. As we can see, a considerable progress in terms of improving the signal-to-noise ratio is made, especially for channels 2 and 3. The waveform of the PLB burst is perfectly recovered from the noise. The implemented algorithm allows also the calculation of the AE features, for instance the Duration-Amplitude graphs can be represented in Figure 7 where the saturation of hits at the predefined maximum duration is now eliminated. AE information is greatly revealed and the hits associated to the PLB bursts can be easily identified.

CONCLUSION

The problem of continuous acoustic emission in CFRP composites was addressed in this paper. Continuous signals caused by an in-service like environment are post-processed using a developed algorithm. This latter includes some successive steps allowing the denoising of the raw signals, the hit detection and separation, and the feature extraction. The implemented method was tested on AE signals derived from experimental procedures consisting in a PLB applied on the surface of a composite ring. These tests were carried out under low and high noise levels in order to assess the robustness of the method.

It was found that the developed method was able to improve the signal-to-noise ratio under different working conditions if the denoising parameters had been properly set. After performing the hit determination and the feature extraction, it was found that the calculated features, which had been erroneous due to the noise, became coherent and exploitable. This AE signal method could be applied on realistic transients retrieved for example from a fatigue test on the composite ring in order to assess the reliability of the method. Such test case will be addressed in future works.

Figure 1 :

 1 Figure 1 : Principle of the AE signal processing method.

Figure 2 :

 2 Figure 2 : Test configuration.

Figure 3 :Figure 4 :

 34 Figure 3 : Duration vs. Amplitude graphs of the detected hits by the AE system from the four channels after a PLB under a low noise level.

Figure 5 :

 5 Figure 5 : Duration vs. Amplitude graph of the detected hits from all channels after a PLB under a high noise level.

Figure 6 :

 6 Figure 6 : Comparison of the raw and denoised signals of the four channels after a PLB under a high noise level. ( ) Raw signals; ( ) denoised signals; and ( ) threshold level.

Figure 7 :

 7 Figure 7 : Duration vs. Amplitude graph of the detected hits after denoising the four signals of the PLB test under a high noise level.

Table 1 :

 1 AE system setup parameters. MS/s 60 µs 120 µs 300 µs 200 ms

	Threshold Pre-Ampli.	Analog Filter	S. Rate PDT	HDT	HLT	Max. Dur.
	40 dB	20 dB	20 kHz -1 MHz 2

July 8-11, 2014. La Cité, Nantes, France Copyright © Inria (2014)

EWSHM 2014 -Nantes, France

ACKNOWLEDGMENT

The authors gratefully acknowledge the Laboratory of Excellence ACTION for its financial support through the program Investments for the future managed by the National Agency for Research (references ANR-11-LABX-01-01).