Karine Alain
email: karine.alain@univ-brest.fr

Anne Postec

Elodie Grinsard

Françoise Lesongeur

Daniel Prieur

Anne Godfroy

a thermophilic, chemolithoautotrophic, sulfate-reducing bacterium isolated from a Mid-Atlantic Ridge hydrothermal vent

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Over the past decades, microbiological investigations of a range of high-temperature marine and terrestrial environments have revealed the presence of a phylogenetically, metabolically and physiologically diverse community of thermophilic prokaryotes endemic to these particular habitats. Among the ubiquitous thermophilic taxa, members of the class Thermodesulfobacteria are commonly retrieved in hot biotopes, regardless the geographic provenance of the samples [START_REF] Skirnisdottir | Influence of sulfide and temperature on species composition and community structure of hot spring microbial mats[END_REF][START_REF] Nakagawa | Molecular characterization of community structures and sulfur metabolism within microbial streamers in Japanese hot springs[END_REF][START_REF] Nakagawa | Variability in microbial community and venting chemistry in a sediment-hosted backarc hydrothermal system: impacts of subseafloor phase-separation[END_REF].

So far, the class Thermodesulfobacteria comprises only the family Thermodesulfobacteriaceae.

Representatives of this family have been isolated from terrestrial geothermal hot springs, petroleum reservoirs and deep-sea hydrothermal vents located worldwide [START_REF] Zeikus | Microbial ecology of volcanic sulphidogenesis: isolation and characterization of Thermodesulfobacterium commune gen. nov. and sp[END_REF][START_REF] Jeanthon | Thermodesulfobacterium hydrogeniphilum sp. nov., a thermophilic, chemolithoautotrophic, sulfate-reducing bacterium isolated from a deep-sea hydrothermal vent at Guaymas Basin, and emendation of the genus Thermodesulfobacterium[END_REF][START_REF] Kashefi | Use of Fe(III) as an electron acceptor to recover previoulsy uncultured hyperthermophiles: isolation and characterization of Geothermobacterium ferrireducens gen[END_REF][START_REF] Moussard | Thermodesulfatator indicus gen. nov., sp. nov., a novel thermophilic chemolithoautotrophic sulfate-reducing bacterium isolated from the Central Indian Ridge[END_REF]. Two recognized genera are currently described within the family Thermodesulfobacteriaceae [START_REF] Boone | sp. nov., a new genus of rod-shaped crenarchaeote isolated from a hot spring in the Philippines[END_REF], namely the genus Thermodesulfobacterium [START_REF] Zeikus | Microbial ecology of volcanic sulphidogenesis: isolation and characterization of Thermodesulfobacterium commune gen. nov. and sp[END_REF] and the genus Thermodesulfatator [START_REF] Moussard | Thermodesulfatator indicus gen. nov., sp. nov., a novel thermophilic chemolithoautotrophic sulfate-reducing bacterium isolated from the Central Indian Ridge[END_REF]. These genera comprise exclusively thermophilic, anaerobic, chemoorganotrophic or chemolithoautotrophic sulfate-reducing strains.

In addition, this family encompasses also the not-formally described species 'Geothermobacterium ferrireducens' that is unable to reduce sulphate [START_REF] Kashefi | Use of Fe(III) as an electron acceptor to recover previoulsy uncultured hyperthermophiles: isolation and characterization of Geothermobacterium ferrireducens gen[END_REF].

The genus Thermodesulfatator is so far composed of the unique species T. indicus, the type species being a strict chemolithoautotrophic sulfate-reducer that was isolated from a deep-sea hydrothermal vent from the Kairei vent field on the Central Indian Ridge [START_REF] Moussard | Thermodesulfatator indicus gen. nov., sp. nov., a novel thermophilic chemolithoautotrophic sulfate-reducing bacterium isolated from the Central Indian Ridge[END_REF]. In this study, a novel hydrothermal bacterium belonging to the genus Thermodesulfatator is described.

In June 2001, during the ATOS oceanographic cruise, fragments of active hydrothermal chimney rocks were collected from 2275m depth at the Rainbow vent field, on the Mid-Atlantic Ridge (36°13'N, 33°54'W).

Sample collection, subsampling and storage procedures were as described elsewhere [START_REF] Postec | Continuous enrichment culture and molecular monitoring to investigate the microbial diversity of themophiles inhabiting deep-sea hydrothermal ecosystems[END_REF].

All subsamples were pooled and used to inoculate a 2L gas-lift bioreactor (inoculum 2% v/v). A continuous enrichment culture was performed in this bioreactor for 41 days, at 60°C and pH 6.5, on a complex medium (modified SME medium) containing sea water salts (including sulfate), minerals, carbohydrates, peptides, organic acids (acetate and pyruvate), and colloidal sulfur [START_REF] Postec | Continuous enrichment cultures : insights into prokaryotic diversity and metabolic interactions in deep-sea vent chimneys[END_REF]. This continuous culture was performed, at a dilution rate of 0.04 h -1 (80 mL h -1 ), and under a stream of N 2 (0.1 v v -1 min -1 ) to maintain anaerobic conditions and to drain possible inhibitors. Then, a culture sample from day 28 was used as an initial inoculum on TYA medium (60°C, pH 6.0, sulfate 9.3 mM, atmosphere of H 2 /CO 2 80/20 2 bars), as described by [START_REF] Postec | Continuous enrichment cultures : insights into prokaryotic diversity and metabolic interactions in deep-sea vent chimneys[END_REF]. One strain was purified by both isolation on solid medium (TYA medium solidified with 1.5 % Phytagel incubated in anaerobic jars under H 2 /CO 2 80/20 2 bars) and then repeated dilutions-to-extinction series. This strain referenced as strain AT1325 T is described in this publication.

Purity of this isolate was confirmed routinely by microscopic examination and by repeated partial sequencing of the 16S rRNA gene using several primers. Stock cultures were stored at ─80°C in TYA medium supplemented with 5% (v/v) DMSO.

The 16S rRNA gene (1491 bp) of the novel isolate was double-strand sequenced as described elsewhere [START_REF] Alain | Caminibacter hydrogeniphilus gen. nov., sp. nov., a novel thermophilic, hydrogen-oxidizing bacterium isolated from an East Pacific Rise hydrothermal vent[END_REF]. This sequence was first compared to those in available databases by use of the BLAST program [START_REF] Altschul | Basic local alignment search tool[END_REF]. It was then aligned to its nearest neighbours using the CLUSTALX program [START_REF] Thompson | The ClustalX windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools[END_REF]. The alignment was refined manually using the SEAVIEW program [START_REF] Galtier | SEAVIEW and PHYLO_WIN: two graphic tools for sequence alignment and molecular phylogeny[END_REF] [START_REF] Saitou | The neighbour-joining method: a new method for reconstructing phylogenetic trees[END_REF] and maximum likelihood ML [START_REF] Felsenstein | Evolutionary trees from DNA sequences: a maximum likelihood approach[END_REF]. The robustness of the inferred topologies was assessed by bootstrap analyses (1000 bootstrap resamplings with NJ and 100 replications with ML) [START_REF] Felsenstein | Confidence limits on phylogenies: an approach using the bootstrap[END_REF].

Comparison of the 16S rRNA gene sequence of strain AT1325 T with sequences of Bacteria revealed a phylogenetic relationship with deeply branching bacterial lineages. Phylogenetic reconstructions indicated that the novel isolate belonged unquestionably to the class Thermodesulfobacteria [START_REF] Garrity | Phylum BIII. Thermodesulfobacteria phy. nov. In Bergey's Manual of Systematic Bacteriology[END_REF] and more especially to the family Thermodesulfobacteriaceae, order Thermodesulfobacteriales ( et al., 2002). Within this lineage, the novel isolate clustered robustly with T. indicus [START_REF] Moussard | Thermodesulfatator indicus gen. nov., sp. nov., a novel thermophilic chemolithoautotrophic sulfate-reducing bacterium isolated from the Central Indian Ridge[END_REF], an other deep-sea hydrothermal vent isolate (Fig. 1). Both isolates shared 97.8% 16S rRNA gene sequence identity. Strain AT1325 T was most distantly related to members of the genera Thermodesulfobacterium and 'Geothermobacterium', sharing 87.7 to 88.7% 16S rRNA gene sequence identity with representatives of these genera. Based on the sequence identity and phylogenetic analyses, the novel isolate could be assigned to the genus Thermodesulfatator. The level of 16S rRNA gene sequence dissimilarity with

Hatchikian
Thermodesulfatator indicus also showed that the novel isolate displayed sufficient molecular differences for a delineation at the species-level [START_REF] Stackebrandt | Taxonomic parameters revisited: tarnished gold standards[END_REF]. Indeed, the sequence similarity between the 16S rRNA genes of T. indicus and strain AT1325 is far below the threshold value (98.7-99%) currently recommended to perform DNA-DNA hybridization in order to test for the genomic uniqueness of a novel isolate [START_REF] Stackebrandt | Taxonomic parameters revisited: tarnished gold standards[END_REF].

Morphological characteristics of cells of strain AT1325 T were determined by light microscopy (Olympus CX40), transmission electron microscopy (Jeol JEM 100 CX II) and scanning electron microscopy (FEI Quanta 200). Cells were straight rods of 1.0-6.1 µm in length (mean 2.4 µm ± 1.5, n=10) and 0.3-0.8 µm in width (mean 0.6 ± 0.1, n=10) in the mid-exponential phase of growth (see supplementary Fig. S1A and S1B in IJSEM Online). They stained Gram-negative. Cells occurred mainly singly and divided by constriction (Fig. S1A). They possessed single polar flagellum (Fig. S1B) and were highly motile. Formation of spores was never observed.

The physiological characterization of the novel isolate was carried out in a basal medium referenced as "SO4PNsalts" and containing (per liter): 0.33 g NH 4 Cl, 0.5 g KCl, 0.5 g CaCl 2 .2H 2 O, 3 g MgCl 2 .6H 2 O, 22 g NaCl, 3.0 g Na 2 SO 4 , 5 g PIPES buffer (Sigma) and 1 mg resazurin (Sigma). Its pH was adjusted to 6.7. Once prepared, this medium was autoclaved and cooled to room temperature under a O 2 -free N 2 gas flow. Then, 1 ml vitamin mixture [solution from [START_REF] Widdel | Gram-negative mesophilic sulfate-reducing bacteria[END_REF] supplemented with 4 mg folic acid and 1.5 mg lipoic acid], 1 ml thiamine solution (Widdel & Bak,1992), 1 ml selenite-tungstate solution [START_REF] Widdel | Gram-negative mesophilic sulfate-reducing bacteria[END_REF]) and 1 ml non-chelated trace element mixture [START_REF] Widdel | Gram-negative mesophilic sulfate-reducing bacteria[END_REF] were added to the basal medium from concentrated anaerobic filter-sterilized solutions. Finally, KH 2 PO 4 was injected from sterile stock to a final concentration of 40 mM. Medium (10 ml) was then dispensed anaerobically in 50 ml vials sealed with butyl-rubber stoppers and reduced with 0.1 ml of a 10% (w/v) Na 2 S.9H 2 O sterile solution, just before inoculation. Unless stated otherwise, the experiments were carried out anaerobically, under a gas phase of H 2 /CO 2 (80/20; 200 kPa), and incubation were done in the dark, at 65°C.

Growth was routinely monitored by direct cell counting using a modified Thoma chamber (depth 10 µm), or by counting after fixation with 1% (v/v) glutaraldehyde and storage at -20°C. Growth rates were calculated using linear regression analysis of five to nine points along the linear portions of the growth curves logarithmically-transformed. The determination of the temperature range for growth was tested over the range 50-85°C (i.e. 50, 55, 60, 65, 70, 75, 80 and 85°C). No growth was observed at 50°C, 80°C and above.

The novel isolate grew from 55 to 75°C, with an optimum growth rate at 65-70°C (see Supplementary Fig.

S2 in IJSEM Online

). The pH range for growth was tested from initial pH 4.0 to initial pH 9.0, at 65°C, in basal medium buffered and adjusted to the required pH (initial pH at 20 °C) as described elsewhere [START_REF] Alain | Caminibacter hydrogeniphilus gen. nov., sp. nov., a novel thermophilic, hydrogen-oxidizing bacterium isolated from an East Pacific Rise hydrothermal vent[END_REF]. Growth was observed from pH 5.5 to pH 8.0, the optimum being around pH 6.5-7.5. No growth was observed at pH 5.0 and below, neither at pH 8.5 and above. Salt tolerance was tested at 65°C in 'SO4PNsalts' medium prepared with various concentrations of NaCl (0, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 6.0, 8.0 and 10% w/v). Growth was observed at salt concentrations ranging from 1.5 to 4.5% (w/v) NaCl, the optimum salinity being around 2.5%. No growth was observed at 1 and 5 % (w/v) NaCl. Under optimal growth conditions, the generation time of strain AT1325 T was around 3 hours and 20 minutes.

The determination of the whole-cell fatty acid composition was performed on cultures grown at 70°C on "SO4PNsalts" medium, under a gas phase of H 2 /CO 2 (80/20; 200 kPa). The production of biomass was done both in media prepared with and without 0.1 g L -1 yeast extract. In both cases, cells were harvested at the end of the exponential phase of growth. In parallel, Thermodesulfatator indicus str. CIR29812 T was grown exactly under the same conditions and in the same culture medium in order to compare its PFLA pattern.

The analyses were carried out at the DSMZ according to the standard protocol of the Microbial Identification System (MIDI Inc., Del. USA, 2001). Extracts were analysed using a Hewlett Packard model HP6890A gas chromatograph equipped with a flame-ionization detector as described by [START_REF] Kämpfer | Numerical analysis of fatty acid patterns of coryneform bacteria and related taxa[END_REF]. Results are detailed in Table 1. Similarly to T. indicus, the fatty acid profile of strain AT1325 T comprised hydroxylated fatty acids, cyclic fatty acids, saturated and unsaturated straight chain fatty acids, that consisted mainly of C 16:0 , C 18:0 and C 18:1 ω7c. However, the fatty acids profiles of the novel isolate and of T. indicus str. CIR29812 T grown exactly under the same conditions displayed few minor differences as shown in Table 1.

Strain AT1325 T was a strict anaerobic, chemolithoautotrophic bacterium that used hydrogen and sulfate as respective primary electron donor and acceptor. Its ability to use alternative electron acceptors was tested by adding elemental sulfur (12 g l -1 ), L-cystine (12 g l -1 ), sulfite (1 mM), thiosulfate (20 mM), nitrate (10 mM), nitrite (1 mM) or oxygen (1% v/v) to sulfate-depleted media, under a H 2 /CO 2 atmosphere (80/20; 200 kPa).

Quantitative determination of hydrogen sulphide was determined as described elsewhere [START_REF] Cord-Ruwisch | A quick method for the determination of dissolved and precipitated sulfides in cultures of sulfate-reducing bacteria[END_REF]. The novel isolate was found to reduce sulfate to H 2 S, but did not grow when sulfur, L-cystine, sulfite, thiosulfate, nitrate, nitrite and oxygen were used as electron acceptors. For a sulfate-reducer, the inability to use sulfite as sole terminal electron acceptor is surprising but is not an exception within the microbial world (Itoh et al., 1999;[START_REF] Jeanthon | Thermodesulfobacterium hydrogeniphilum sp. nov., a thermophilic, chemolithoautotrophic, sulfate-reducing bacterium isolated from a deep-sea hydrothermal vent at Guaymas Basin, and emendation of the genus Thermodesulfobacterium[END_REF][START_REF] Moussard | Thermodesulfatator indicus gen. nov., sp. nov., a novel thermophilic chemolithoautotrophic sulfate-reducing bacterium isolated from the Central Indian Ridge[END_REF]. To examine possible carbon sources other than CO 2 , a variety of organic carbon sources were tested in the presence of sulfate, under an atmosphere of H 2 100% (200 kPa). Formate (10 mM), acetate (20 mM), propionate (20 mM), methanol (0.5% v/v), pyruvate (10 mM), glucose (20 mM), monomethylamine (10 mM), peptone (0.2 g l -1 ) and yeast extract (0.2 g l -1 ) were tested as potential substrates. Significant growth coupled to hydrogen sulphide production was still observed after three transfers on the same medium (inoculation to 1/200 in all cases) when monomethylamine, peptone or yeast extract were provided as sole carbon source and when H 2 and sulfate were the respective electron donor and acceptor. Under the conditions tested, formate, acetate, propionate, methanol, pyruvate and glucose could not be used as sole carbon source. In order to compare the physiological capabilities of the novel isolate to the ones of its closest relative Thermodesulfatator indicus str. CIR29812 T , with respect to carbon source utilization, carbon source tests were performed in parallel with T. indicus under exactly the same conditions. Under our experimental conditions, T. indicus str. CIR29812 T did not grow when monomethylamine, peptone or yeast extract were provided as sole carbon source and when H 2 and sulfate were the respective electron donor and acceptor. To test for the capability of the strain AT1325 T to use electron donors other than molecular hydrogen, the strain was cultivated under a gas phase of N 2 /CO 2 (80/20%, 200 kPa) in the presence of formate (10 mM), acetate (20 mM), butyrate (10 mM), lactate (10 mM), methanol 0.5% (v/v), yeast extract (0.2 g l -1 ) and peptone (0.2 g l -1 ) with sulfate as a terminal electron acceptor. No growth was observed with the alternative energy sources, indicating that strain AT1325 T was a strict hydrogen-oxidizer. Finally, the nitrogen sources for growth were also examined in a nitrogen-depleted medium. The novel isolate was found to grow on organic and inorganic nitrogen sources. Significant growth was observed when NH 4 Cl (20 mM), glutamate (10 mM), yeast extract (0.2 g l - 1 ), tryptone (0.2 g l -1 ), gelatin (0.05% v/v) and urea (0.05% v/v) were provided as sole nitrogen source.

Antibiotic resistance was tested in the presence of a variety of antibiotics from different chemical nature and with different targets and mechanisms. The resistance to vancomycin, streptomycin, chloramphenicol, kanamycin, rifampicin, penicillin G, ampicillin and tetracycline was investigated at concentrations of 10, 50, 100 and 200 µg ml -1 . When the antibiotic was diluted in ethanol (chloramphenicol) or DMSO (rifampicin), the same volume of solvent was added to control cultures rather than water. All antibiotics were added aseptically before inoculation and the cultures were incubated at 65°C for one week. Strain AT1325 T was found to be sensitive to 10 µg ml -1 of ampicillin and penicillin G. It grew in the presence of 10 µg ml -1 vancomycin and tetracycline, of 50 µg ml -1 rifampicin and chloramphenicol, but was sensitive to higher concentrations of these four antibiotics. The novel isolate was resistant to 200 µg ml -1 streptomycin and kanamycin.

The G+C content of the genomic DNA was determined from the melting point according to [START_REF] Marmur | Determination of the base composition of desoxyribonucleic acid from its thermal denaturation temperature[END_REF], as described elsewhere [START_REF] Alain | Caminibacter hydrogeniphilus gen. nov., sp. nov., a novel thermophilic, hydrogen-oxidizing bacterium isolated from an East Pacific Rise hydrothermal vent[END_REF]. A calibration curve was constructed by use of ultrapure DNA from Clostridium perfringens (26.5 mol% G+C), calf thymus (42 mol% G+C), Escherichia coli strain B (50 mol% G+C) and Micrococcus luteus (72 mol% G+C) as standards (Sigma). The G+C content of strain AT1325 T was 45.6 mol%.

In summary, the novel isolate shares many physiological, chemotaxonomic and metabolic properties with its closest relative T. indicus. Its phenotypic and genotypic properties generally met the characteristics described for the genus Thermodesulfatator [START_REF] Moussard | Thermodesulfatator indicus gen. nov., sp. nov., a novel thermophilic chemolithoautotrophic sulfate-reducing bacterium isolated from the Central Indian Ridge[END_REF]. Indeed, strain AT1325 T is a marine, thermophilic, strictly anaerobic bacterium growing chemolithoautrophically from H 2 oxidation and using sulfate as sole electron acceptor. It robustly branches with the unique representative of the genus Thermodesulfatator, namely T. indicus. Nevertheless, strain AT1325 T can be distinguished from T. indicus and from other Thermodesulfobacteriaceae species in terms of a number of genotypic and physiological features detailed in Table 2. In brief, the novel taxon differs from T. indicus by its clear phylogenetic distance and its broader pH range for growth. In addition, the novel isolate is able to use some organic compounds (methylamine, peptone and yeast extract) as sole carbon source while its congener T. indicus is unable. In conclusion, in view of the above-mentioned distinctive features, we propose that the isolate AT1325 T should be assigned as the type strain of a novel species, for which the name Thermodesulfatator atlanticus sp. nov. is proposed.

Description of Thermodesulfatator atlanticus sp. nov.

Thermodesulfatator atlanticus (at.lan'ti.cus. L. masc. adj. atlanticus, from the Atlantic Ocean, referring to the site of isolation of the type strain).

Cells are Gram-negative motile rods (1.04-6.08 µm long by 0.30-0.75 µm wide) with a single polar flagellum. Optimal growth occurs at 65-70°C, with a growth range from 55 to 75°C. The pH and NaCl ranges are 5.5-8.0 (optimum 6.5-7.5) and 1.5-4.5% (w/v) (optimum, 2.5% w/v NaCl), respectively. Growth occurs under strictly anaerobic conditions using H 2 as an electron donor, sulfate as a terminal electron acceptor and CO 2 as a carbon source. Strict hydrogen-oxidizer.

The following are not used as electron donors: formate, acetate, lactate, methanol, peptone and yeast extract. In the presence of H 2 and sulfate, monomethylamine, peptone or yeast extract can be used as sole carbon source. No growth is observed when formate, acetate, propionate, methanol, pyruvate and glucose are provided as sole carbon source. Does not ferment pyruvate or lactate. The following are not utilized as electron acceptors: elemental sulfur, L-cystine, thiosulfate, sulfite, nitrate, nitrite, oxygen. Is able to utilize a wide range of organic and inorganic nitrogen sources.

Antibiotic resistance: resistant to 200 µg ml -1 streptomycin and kanamycin; Sensitive to 10 µg ml -1 ampicillin and penicillin G; sensitive to 50 µg ml -1 vancomycin and tetracycline, and to 100 µg ml -1 rifampicin and chloramphenicol.

Fatty acid profile is mainly composed of C 16:0 , C 18:1 ω7c, C 18:0 and C 19:0 cyclo ω8c. Genomic DNA G+C content of the type strain AT1325 T is 45.6 mol%.

The type strain, AT1325 T (DSM 21156 T , JCM 15391 T ) was isolated from the walls of an active deep-sea hydrothermal vent chimney at the Rainbow vent field, on the Mid-Atlantic Ridge (36°13'N, 33°54'W). It is also available under request at the "Souchothèque de Bretagne" (catalogue LMBE) culture collection (http://www.ifremer.fr/souchotheque/). Thermodesulfatator indicus [START_REF] Moussard | Thermodesulfatator indicus gen. nov., sp. nov., a novel thermophilic chemolithoautotrophic sulfate-reducing bacterium isolated from the Central Indian Ridge[END_REF]; 3, Thermodesulfobacterium hydrogeniphilum [START_REF] Jeanthon | Thermodesulfobacterium hydrogeniphilum sp. nov., a thermophilic, chemolithoautotrophic, sulfate-reducing bacterium isolated from a deep-sea hydrothermal vent at Guaymas Basin, and emendation of the genus Thermodesulfobacterium[END_REF]; 4, Thermodesulfobacterium commune [START_REF] Zeikus | Microbial ecology of volcanic sulphidogenesis: isolation and characterization of Thermodesulfobacterium commune gen. nov. and sp[END_REF]; 5, 'Geothermobacterium ferrireducens' [START_REF] Kashefi | Use of Fe(III) as an electron acceptor to recover previoulsy uncultured hyperthermophiles: isolation and characterization of Geothermobacterium ferrireducens gen[END_REF].

TABLES and FIGURES

Legend: +, positive; ─, negative; ND, not determined. The percentage of 16S rRNA gene sequence identity is calculated in reference to the 16S rRNA gene sequence of the novel isolate AT1325 T .

  on 16S rRNA gene sequences placed strain AT1325 T within the family Thermodesulfobacteriaceae, in the bacterial domain. Comparative 16S rRNA gene sequence analysis indicated that strain AT1325 T belonged to the genus Thermodesulfatator, sharing 97.8% 16S rRNA sequence identity with Thermodesulfatator indicus, the unique representative species of this genus. On the basis of phylo-phenetic features, we propose a novel species, Thermodesulfatator atlanticus sp. nov. The type strain is AT1325 T (= DSM 21156 T = JCM 15391 T ).

Fig. 1 .

 1 Fig. 1. Phylogenetic relationships of strain AT1325 T and its closest relatives within the class Thermodesulfobacteria. 16S rRNA gene sequence data of reference strains were obtained from the GenBank/EMBL databases. Accession numbers are indicated in brackets. The topology shown corresponds to an unrooted tree obtained by the Maximum Likelihood algorithm, established using the PHYLIP package. Bootstrap values (from 100 replicates) are indicated at the branch nodes. The positioning of the novel isolate was confirmed by the Neighbour-Joining method. The scale bar indicates 1 nt substitutions per 100 nt.

Table 1 . Whole cell fatty acid profiles of strain AT1325 T and of Thermodesulfatator indicus strain CIR29812 T .

 1

	Fatty acid	Strain AT1325 T grown on
		"SO4PNsalts" medium
		(under a gas phase of H 2 /CO 2
		and without yeast extract)
	Saturated fatty acids
	C 16:0	6.37
	C 17:0	1.12
	C 18:0	16.14
	Monounsaturated fatty acids
	C 16:1 ω5c	0.82
	C 17:1 ω6c	2.23
	C 18:1 ω9c	1.16
	C 18:1 ω7c	59.43
	C 18:1 ω5c	2.07
	C 20:1 ω7c	1.23
	Hydroxyl fatty acid
	3-OH C 16:0	1.90
	Cyclic fatty acid
	C 19:0 cyclo ω8c	6.40

Values are percentages of total fatty acids. The nomenclature is as follows: the first number indicates the number of carbon atoms in the molecule. The prefixes 'anteiso', 'iso', 'OH' and 'cyclo' indicate anteiso-or iso-branched, hydroxy or cyclic fatty acids. The second number following the colon indicates the number of double bonds. The position of the double bond is indicated by the carbon atom position starting from the methyl (ω) end of the molecule. The suffix c indicates the cis isomer. Summed feature contain one or more of each fatty acid. Summed features: 3, C 16:1 ω7c and/or 2-OH iso-C 15:0 ; 5, C 18:2 ω6,9c and/or anteiso C 18:0 .

Major fatty acids (>5%) are indicated in bold values. ND: not detected.

Fatty acid Both strains were grown exactly under the same conditions on "SO4PNsalts" medium, in the presence of 0.1 g/L yeast extract and under a H2/CO2 atmosphere

	Saturated fatty acids
	C 12:0	0.56	0.40
	C 14:0	1.26	0.76
	C 15:0	ND	0.59
	C 16:0 N alcohol	ND	0.19
	iso C 16:0	0.49	0.39
	C 16:0	21.79	19.61
	anteiso C 17:0	0.22	ND
	C 17:0	2.27	10.04
	iso C 18:0	0.24	ND
	C 18:0	33.87	29.82
	C 19:0	0.27	1.16
	C 20:0	0.81	0.35
	Monounsaturated fatty acids
	C 16:1 ω5c	0.24	0.20
	C 17:1 ω6c	1.88	3.62
	C 17:1 ω8c	0.19	0.35
	C 18:1 iso H	0.39	0.31
	C 18:1 ω9c	4.08	2.68
	C 18:1 ω7c	21.76	21.63
	C 18:1 ω5c	0.77	0.71
	C 18:3 ω6c (6, 9, 12)	0.30	0.28
	C 20:1 ω7c	> max ar/ht	ND
	Hydroxyl fatty acid
	3-OH C 16:0	1.08	0.42
	3-OH C 18:0	0.38	ND
	Cyclic fatty acid
	C 19:0 cyclo ω8c	4.42	4.34
	Summed features
	Summed feature 3	1.29	1.16
	Summed feature 5	1.46	0.97

Table 2 . Characteristics differentiating strain AT1325 T from representative species of the family Thermodesulfobacteriaceae.

 2 Species: 1, Thermodesulfatator atlanticus AT1325 T (this study); 2,

ACKNOWLEDGEMENTS

We thank Marc le Romancer and Philippe Crassous for their assistance with the transmission and scanning electron microscopes. We are grateful to one anonymous referee for his interesting and constructive comments. This work was financially supported by the Région Bretagne and the joined research unit UMR6197, linking the Université de Bretagne Occidentale to the Ifremer and the Centre National de la Recherche Scientifique. We thank the captain and crew of the NO L'Atalante, the pilots and support crew of the ROV Victor and P.-M. Sarradin, Chief Scientist for helping us to collect deep-sea hydrothermal vent samples during the ATOS oceanographic cruise.

The GenBank/EMBL/DDBJ accession


NEW APPROACHES OF THE FORECAST OF THE AGEING OF PLASMA JET NOZZLE IN INDUSTRIAL SETTINGS OF THERMAL SPRAYING

LENAIN Jean-François a , PATEYRON Bernard b a Université de Limoges 123, Albert Thomas 87060 Limoges Cedex.

b Sciences des Procédés Céramiques et Traitements de Surface UMR6638 University of Limoges, CNRS 12, rue Atlantis 87068 Limoges Cedex * (corresponding author: pateyron@unilim.fr)

-Summary

The optimization of the extension of the timelife of the torches without hazard of drop of the product is an unceasing industrial difficulty. It needs a reliable criterion to characterize the wear, and thus indicate when the nozzle used should be switched by a new nozzle. These criteria are still quite subjective and hold the operator's experience. In 2003, David RIGOT, in collaboration with the Volvo Aero Corporation, has defined a number of criteria in his thesis dissertation [START_REF] David | Contribution à l'étude de l'érosion des électrodes de torches à plasma d'arc pour la projection par suivi en ligne des signaux de tension et de son[END_REF]. This study led to the realization of an electronic module alert detecting the hazardous time of process. The focus method was based on research and tracking characteristic frequencies in arc voltage fluctuations and in acoustic noise that is the image. It seems that a rough statistical study of signals is able to help define and visualize simple criteria ruthless variation is usable in objective phenomena wear alert. This is ad hoc measures for smooth power spectrum [START_REF] Simonoff | Smoothing Methods in Statistics[END_REF] [3] and its variability. Filtered signal is used to distinguish typically one or two epochs of slow wear followed by an epoch of fast wear. This difference is clear after a suitable choice of used frequency bands. Graphical representations are very expressive; they are supplemented by statistical tests. Signals & method: results, by David RIGOT on campaign made shots in September 2003 at the aircraft manufacturer Volvo Aero. Files led to collect a series of measures standardized on the time life of the nozzles. For one, whose use was particularly long, a few shots "vacuum" were prosecuted beyond of the usual threshold used in order to study the ageing. Sound torch and tension thus collected at various times of operation nozzles, with several frequencies of sampling, feed and no feed of powder. They allow first approach directly extract global statistical parameters which it represents dissimilarity based on time: moments, skewness, kurtosis, entropy [START_REF]Entropie en traitement du signal[END_REF]. To each power spectra measurement, time is calculated from each signal multiple sampling. Each of the frequencies spectra is smooth inside of a convolution window by a kernel assessor [START_REF] Lenain | Nouvelles approches de l'évaluation du vieillissement des tuyères de torches à plasma en conditions industrielles de projection plasma, 8ème Colloque sur les Arcs Électriques[END_REF]. A simultaneous representation of the power of signals and their erraticism are used to visually choose the most discriminating frequency bands. Two groups of settings are then assessed: mean and variance of spectrum in the band selected; global parameters newly evaluated from filtered in the chosen band signals. Results: most selective statistics evolve in a similar manner and allows periods wear, while the overall results are most often efficient. For example templates to slope failure (piecewise linear models) are used. The spectra of tension frequencies and the spectra of sound frequencies give similar results. The sound is usable with the feed or without feed of powder. Sequential simple tests may be expected to assess the time of rapid wear.

Aim of this study

However the plasma projection industry faces growing performance requirements and more stringent requirements. The answer to these constraints requires a better understanding of the phenomena involved in the process of plasma projection (fluctuations in the foot of arc phenomenon of particle dispersion of powder, interaction plasma-particle, impact of particles on substrate,...), and better tools for diagnosis and prediction (control online and model digital). It is the task that it is proposed the SPCTS laboratory and for which he seeks the cooperation of the scientific community. More than thirty years on the methods of treatment of materials by plasma and its experimental work and simulations have covered the areas of process with successively reactor plasmas [START_REF] Kassabji | Design of furnace 0.7 MW plasma for the reduction of iron oxides[END_REF], [START_REF] Aubreton | Plasma[END_REF], [START_REF] Fauchais | State of the art in the field of plasma spraying and of extractive metallurgy with transferred arc: modelling, measurement, comparison between both, applications and developments[END_REF], [START_REF] Lerrol | Dimensional study of used reactor plasma transferred arc[END_REF], the study of chemical thermodynamics and transport in plasma properties and the database T&TWinner [START_REF] Pateyron | Code ADEP -chemistry on Minitel" the Journal of CNRS May 1992[END_REF] , [START_REF] Pateyron | Fauchais Study of the behaviour of the heat conductivity and other transport properties of a simple reacting system: H2 -Ar and H2-Ar-air. Dilution effect in spraying process at atmospheric pressure[END_REF], [START_REF] Pateyron | Fauchais Thermodynamic and transport properties of Ar -H2 and Air-H2-Ar plasma gases used for spraying at atmospheric pressure Plasma Chemistry Plasma Processing[END_REF], [START_REF] Pateyron | Fauchais Sound velocity in different reacting thermal plasma coatings Plasma[END_REF], [START_REF] Chen | Fauchais Thermodynamic and transport properties of argon/helium plasmas at atmospheric pressure[END_REF][, [START_REF] Benettouil | Predicting dynamic and thermal histories of agglomerated particles injected within a dc plasma jet[END_REF], [START_REF] Leger | A new ternary mixture to improve the properties of plasma sprayed ceramic coatings[END_REF], [START_REF] Pateyron | The forecast of the chemical and transport properties of a carbon-oxygen plasma in isochoric conditions[END_REF] electric arc [START_REF] Fauchais | the production of thermal plasmas[END_REF], [START_REF] Afifi | Modélisation tridimensionnelle de l'histoire thermique de matériaux chauffés par un jet plasma. Application à la projection plasma et aux traitements thermiques[END_REF] plasma jet training, design and flare plasma metrology [START_REF] Jestin | Dimensionless study of experimental pressure related behavour for two powerful vortex blown arc and vortex-stabilzed power plasma torches[END_REF], [START_REF] Brilhac | Fauchais Investigation of the thermal characteristics of d.c. vortex plasma torches High Temp[END_REF], [START_REF] Brilhac | Bouvier Study of the dynamic and static behavior of DC vortex plasma torches: Part 1: Button type cathode Plasma chemistry and plasma processing[END_REF], [START_REF] Brilhac | Bouvier Study of the dynamic and static behavior of DC vortex plasma torches: Part 2: Well-type cathode Plasma chemistry and plasma processing[END_REF], [START_REF] Fataoui | Simplified 2D thermo-mechanical modeling of splat formation in plasma spraying processes[END_REF], the treatment of particles flying [START_REF] Delluc | A numerical tool for plasma spraying. Part I : modelling of plasma jet and particle behavior[END_REF], [START_REF] Mariaux | Modeling of the plasma spray process: From powder injection to coating formation[END_REF] and their impact on the substrate [START_REF] Cedelle | Experimental investigation of the splashing processes at impact in plasma sprayed coating formation[END_REF], [START_REF] Cedelle | Investigation of plasma sprayed coatings formation by visualization of droplet impact and splashing on a smooth substrate[END_REF], [START_REF] Delluc | A numerical tool for plasma spraying. Part II : model of statistic distribution of alumina multi particle powder[END_REF] , [START_REF] Ganaoui | Thermal modelling of multilayer sprayed particles under plasma conditions[END_REF], [START_REF] Cedelle | Study Of Droplet Behaviour At Impact In Plasma Spraying High Temp[END_REF]. This production was possible because the laboratory still is equipped with means of diagnosis and control online and it has developed digital variable complexity tools he needed.

Thermal projection industry is subject to high stresses of reproducibility and reliability of deposits since the parts to have a high added value. The controls in the deposit lines naturally took an important place during recent years. In effect the erosion of electrode in plasma flare a decrease of the enthalpy of the plasma flame and consequently decreased speed and the temperature of the projected particles. This modifies the final properties of the deposit in the best of cases, and leads to its destruction by incorporation of metal particles of the electrodes in the case of the latter too much wear. Various online techniques that exist currently consist mainly to follow the change of temperature and speed of the particles in flight or hot particle flux distributions. It is an indirect method to estimate the effectiveness of a torch and correct the effects of the erosion of the electrodes on the heating and acceleration of the particles. But there is not this update method to directly diagnose the erosion of the electrodes. Numerous studies have already shown that the voltage at the terminals of the electrodes changes their State of wear, but no theory has yet developed to help understand the phenomena of erosion and no apparatus for direct control of the State of the electrodes is available at the present time. Very soon the scientific community has studied these problems of electrode wear [START_REF] Pateyron | Contribution à la réalisation et à la modélisation de réacteurs plasmas soufflés ou transférés appliqués à la production de poudres ultrafines métalliques ou céramiques[END_REF][START_REF] Szente | Erosion of plasma torch electrodes[END_REF], without however. This is to compensate for this deficiency that Volvo Aero Corporation and the SPCTS laboratory joint their efforts to support the work of David Rigot [START_REF] David | Contribution à l'étude de l'érosion des électrodes de torches à plasma d'arc pour la projection par suivi en ligne des signaux de tension et de son[END_REF] on the following objectives: determine indicators parameters of the State of a torch at a time given its use, follow the evolution of these different indicators and select those that are most relevant to control the erosion of electrode wear. The erosion of the nozzle is attributed to the permanent displacement of the foot of arc on the surface of the electrode as a result of magneto hydrodynamic forces. These fluctuations in the foot of arc translate by fluctuations of the voltage at the terminals of the electrodes and strongly contribute to the emission of the sound by the torch [START_REF] Ingard | Acoustic wave generation and amplification in a plasma[END_REF][START_REF] Pauvit | Spectral and acoustic diagnostic of a plasma torch[END_REF][START_REF] Dadgar | Emission d'ondes acoustiques dans un arc électrique[END_REF][START_REF] Fitaire | Bruits acoustiques des arcs électriques" L'arc électrique et ses applications[END_REF]. These two measurable phenomena are studied. The difficulty lies in the difference of time scales between the fluctuations of the foot of arc (in the area of the 100 to 500µs) and the development of erosion (in the tens of hours). Here a new statistical analysis is presented which tends to resolve this problem.

-Materials and Methods

Record of experimental data

The experiment was carried out between September and October 2002 by D. Rigot [START_REF] David | Contribution à l'étude de l'érosion des électrodes de torches à plasma d'arc pour la projection par suivi en ligne des signaux de tension et de son[END_REF]38,[START_REF] Rigot | Study of the erosion of electrodes in D.C; plasma torches by on line monitoring of the voltage and the sound[END_REF][START_REF] Rigot | Contribution à l'étude d'un modèle thermique lié à la stagnation du pied d'arc sur l'anode, dans un torche de projection plasma[END_REF] within the Volvo Aero Corporation SE -461 81 Trollhättan, Sweden, in real industrial conditions. On the four plasma spray torches that were in use at this time (Figure 1 and Figure 2), two of them reach their end of life and give sufficient data. Plasma torch 1 had a regular lifetime of 36 hours while torch 4 had an outstanding long lifetime of 47.7 hours.

The starts were quite regular in this experiment, so that we are unable to differentiate absolute time from relative time such as the number of starts. The data collected at each shooting were the sound emitted by the torch and the torch voltage between electrodes. The data analysis was carried out using Matlab ® R12 (41) and Systat ® 10.2 [START_REF]SYSTAT Statistical Software[END_REF].

Sound was sampled at 8000Hz and recorded in a standardised manner (Figure 3 ): one record with powder feed, the other one without powder feed. Recordings last each one a few to about ten minutes. There were saved first by D. Rigot [START_REF] David | Contribution à l'étude de l'érosion des électrodes de torches à plasma d'arc pour la projection par suivi en ligne des signaux de tension et de son[END_REF] in the wave file "*.wav" format.

For the purpose of this study, we extract from the middle of each recording a subset of 2 minutes 20 seconds holding more than 2^20 data and transformed it to the Matlab format with the free software Audacity 1.2.4. Voltage data were sampled with a digital signal processor and Labview ® 6.1 at 5000Hz for one second and at 50000Hz for 0.1 second. The Matlab load function is used to exploit the recorded text files. In this paper all the data are analysed after rescaling of amplitudes of the signal samples between -1 and +1 limits. 1 1 / 0 9 / 2 0 0 2 1 2 / 0 9 / 2 0 0 2 1 3 / 0 9 / 2 0 0 2 1 4 / 0 9 / 2 0 0 2 1 5 / 0 9 / 2 0 0 2 1 6 / 0 9 / 2 0 0 2 1 7 / 0 9 / 2 0 0 2 1 8 / 0 9 / 2 0 0 2 1 9 / 0 9 / 2 0 0 2 2 0 / 0 9 / 2 0 0 2 2 1 / 0 9 / 2 0 0 2 2 2 / 0 9 / 2 0 0 2 2 3 / 0 9 / 2 0 0 2 2 4 / 0 9 / 2 0 0 2 2 5 / 0 9 / 2 0 0 2 2 6 / 0 9 / 2 0 0 2 2 7 / 0 9 / 2 0 0 2 2 8 / 0 9 / 2 0 0 2 2 9 / 0 9 / 2 0 0 2 3 0 / 0 9 / 2 0 0 2 0 1 / 1 0 / 2 0 0 2 0 2 / 1 0 / 2 0 0 2 0 3 / 1 0 / 2 0 0 2 0 4 / 1 0 / 2 0 0 2 0 5 / 1 0 / 2 0 0 2 0 6 / 1 0 / 2 0 0 2 0 7 / 1 0 / 2 0 0 2 0 8 / 1 0 / 2 0 0 2 0 9 / 1 0 / 2 0 0 2 1 0 / 1 0 / 2 0 0 2 date

Working hours vs Date

Working hours (h)

PT1

PT2 PT4

Experimental data analysis

Some potential criterions of anode erosion are computed from whole and filtered data of each shooting. Filtering is definite on the basis of the graphic representation of both trend and variance of power spectra. Smoothing greatly reduces the huge variability of spectra so that one can observe larger variations and sampling variance (Figure 4). So the more convenient frequency bands are easily defined. In short we get statistics from the original and filtered signals and from the corresponding whole and partial smoothed spectra (Figure 5).

Each curve corresponds to one independent subset of data. Each subset is centred and its power spectrum is obtained by Fast Fourier Transform (FFT). Filtering is carried out by inverse FFT of the selected frequency band. Smoothing is performed by the Nadaraya-Watson (NW) kernel estimator. Consider the power p j at frequency f j . The NW estimate of p j is given by:

NW(p j ) =  i P i K h (f i - f j ) /  i K h (f i - f j )
where K is the weighting function and h a parameter that control the width of the smoothing window. Here K is the Gaussian kernel

K h (x) = h -1 (2π) -1/2 exp(-x 2 /h 2 )
witch was chosen for its smoothness. This calculus is quite slow but it may be preferred to fast convolution algorithms to avoid the effect of rounding errors [ 43 ]. The simple NW estimator may be replaced by other local estimators [START_REF] Wand | Kernel Smoothing[END_REF]. There are also many alternatives to the Gaussian kernel [START_REF] Simonoff | Smoothing Methods in Statistics[END_REF]. For sounds, we used 64 independent samples of 2^14 data (~2 sec.). The window smoothing parameter h was about 40-60 Hz for visualisation but half for calculus. For visualisation purpose, the number of estimates must be important enough for a smoothed representation. For statistics we want to neglect the covariances induced by smoothing, so we need a distance more than 4*h between point estimates with the Gaussian kernel. With h=20 Hz we can estimate up to 50 points between 0 and 4000 Hz and no more than 6 points for a 500 Hz band for example. In the case of tension, the available data were lower with only 2^12 data (~ 0.82 sec.) for each of 30 samples. We used a parameter h=30 Hz for smoothing, and so we estimate 41 points for the whole sample and 3 points for a 400 Hz band. The statistics taken into account are entropy and centred moments. The entropy is evaluated from 63 intervals between the -1 and +1 limits: ENT = - k p k log(p k ) where p k is the proportion of data falling in interval k. The centred moment of order k of n data {x i } is defined as

Y 0 =  i Y i K h (X i -x 0 ) /  i K h (X i -x 0 )
M k = n -1 | i (x i -m) k | 1/k
 Variance, skewness and kurtosis can be deduced for k=2, 3, 4 but we prefer centred moments because they are on the same scale.

Multiple sampling and smoothing of the spectra allow evaluating the local mean and variance (VAR) for any frequency. For a frequency band, we got an estimate of the mean (the mean of means) and of the conditional variance (CVAR, the mean of the local VAR). Smoothing causes covariances between power estimates. In practice a distance between frequencies more than 4h allow to neglect these covariances with the Gaussian kernel.

Erosion modelling, testing and forecasting

To our knowledge, there does not exist to date any established model of anode erosion based on a temporal evolution of objective criterions of wear. The choice of a model will thus be done in a pragmatic way and as simple as possible. The goal is to detect if it exists some threshold in the evolution of the measured statistics. The considerable experimental work of D. Rigot [START_REF] David | Contribution à l'étude de l'érosion des électrodes de torches à plasma d'arc pour la projection par suivi en ligne des signaux de tension et de son[END_REF] allowed us to better target the interesting criterions. Multiple sampling gives the advantage of estimating simultaneously means and variances. This is the basis of statistical modelling and estimation (Figure 5).

A very simple threshold model is the piecewise linear regression model. If we detect two periods in the torch life, we can express a measure y in function of time t as y(t)-y0 = [a(t-t0)]*(t<=t0) + [b(t-t0)]*(t>t0) , where {t0,y0} are the coordinates of the threshold, a and b the trends of y in the two successive periods. Estimation of the four parameters is made by non-linear regression methods. With the aid of multiple sampling, we are able to elaborate a model for the variance of the measures. This will determine the need of a transformation or a weighting of y(t). Weighting is necessary when the variance vary with t or y(t). A transformation is preferred when it simultaneously normalises the error distribution and homogenates the variances so that weighting become unnecessary. It is based on the mean-variance relationship.

When a large deviation is visually detected, we will test it with the prediction limits provided by the linear regression of the mean powers vs. time. For each value of time, the prediction error depends on three terms: a standard deviation SD, the error of the regression (1/n*SD), and the distance of the predicted value. Because we are only interested by positive deviates, we will use unilateral tests. When the error is estimated by the residual error, the upper prediction curve UPC for a given risk is computed on the basis of the Student cumulative function TCF with N-2 degrees of freedom for N measures. For a 95% confidence level (risk=5%) and for a given working time tj, UPC (tj) = [a+b*tj] + TCF(0.95,N-2) * SDP(tj) . SDP(tj) is the standard deviation of the prediction at time tj: SDP(tj) = SD * [ 1+ 1/n + (tj-t)^2 / SUM((ti-t)^2) ]^1/2 , where ti are the observed time values, t the mean of the ti. We will discuss on the choice of the measured error or the estimated residual error for SD. In the case of a known error, the TCF function is replaced by the normal cumulative function NCF.

As piecewise and simple linear models are nested, we may confirm the piecewise model with a likelihood ratio test. When using ordinary least squares, this test reduce to a simple Fisher test between two variances: the mean square of the difference of fitting of the two models and the mean square of the residual error estimate of the more general model, i.e. the piecewise model. Consider H0 as the one piece linear regression model and H1 the two pieces linear regression model estimated with N measures, RSS0 and RSS1 their respective residual sum of squares, p0 and p1 their respective number of parameters. We will calculate the Fischer cumulative function FCF ( [(RSS0-RSS1)/(p1-p0)] / [RSS1/(N-p1)] ) , with (p1-p0) and (N-p1) degrees of freedom, and possibly reject H0 with an error risk=(1-FCF).

Another problem is the prediction of the lifetime of the torches. It is approached here in the simplest manner, were this lifetime is a linear function of the estimated parameters and a power threshold set by the user. We use the function FUNPAR of Systat ® nonlinear estimation procedure to calculate confidence intervals for the predicted lifetime. More advanced methods exist for this inverse problem but they are not presented here.

-Results

The effects of filtering

The useful frequencies are determined visually from the smoothed power spectra. The tension sampled at 50000 Hz gives no results of interest. We easily see stable and homogenate bands between 1500 and 3000Hz for the sound signals and between 1300 and 2200 Hz for the tension signals. We choose the corresponding filtering by keeping the 2000-2500 Hz and 1600-2000 Hz bands, respectively. We are now able to compare the statistics evaluated from unfiltered and filtered signals, and their time dependence (Figure 6):

-from the signals themselves -from their power spectra, whose conditional variances give a confidence interval for each value.

1-Signals:

Filtering improves the relations between erosion and time very clearly (Figure 7). Even moments and entropy evolve in a very similar way in function of time. The shape of the trend allows us to hypothesize two periods of wear: a period of relatively slow erosion followed by a more rapid one. That seems to confirm the existence of a threshold in the phenomena of wear of the torches.

temps (h)

CENTRED MOMENTS ENTROPY

 i (X i -m) 2 ] 1/2  i (X i -m) 3 ] 1/3  i (X i -m) 4 ] 1/4  k p k .

2-Power spectra

The trends obtained from power spectra are similar to those of moments or entropy, but power spectra give improved results: a standard deviation of powers around each mean. So a standardised measure of sound can be related to the anode erosion. The second result is that the sound can be sampled either with or without the presence of powder: the powder injections do not disturb much the sound recordings which remain exploitable in spite of a bigger variability(Figure 8). Filtering is particularly spectacular in the case of tension spectra. After filtering, the tension appears of weaker variance than the corresponding samples of sound. The hypothesised threshold is still more visible. The power seems a little bigger in the presence of powder (Figure 9), but the difference is small. One of the two first measures for the torch 1 is clearly abnormal and will be discarded in the following. We note that the two torches were stopped near the same power value (Figure 10 and Figure 11). The experience of the operator is of great importance. The stopping power value is also about four times the starting one. Two experiments are not sufficient to say if the operator experience can be related to a fixed power or to a relative one.

Statistical modeling 1-Competing models

We want to determinate a threshold as an alert in the erosion process. Even if the true relation seems exponential or power, it can be approximated by linear pieces. We see than a two-piece linear model fits also very well when restricted to the lifetime of the torches and better corresponds to our goal (Figure 12). The long lifetime of torch 4 is explained by a difference of 4 hours between the thresholds but especially by a slower increase of power in the second period. The choice between the three models comes from variance study.

2-Variance modelling

From the usual assumptions that apply to regression and analysis of variance we note: normally distributed error terms, homogeneous variances of different samples, means and variances not correlated. None of these points is checked with the current data. There exist advanced methods that can circumvent some of these problems (iteratively reweighed least squares, general least squares…) but often an adequate variance-stabilizing transformation of the data can solve them all. This transformation is driven from the link between means and variances in log-log scales.

Torch 1 without powder feed

From the kernel estimates of the power spectra, we see that standard deviations (SD) are strongly related to means. They can be modelled by SD = 0.12 * Mean (not represented), while the slope in log-log scales is close to 1 (Figure 13left). This slope suggests logging values to get similar variances along the erosion process. Note that it is not a simple logging of the mean powers: spectra are logged then smoothed so that we get means of logged powers.

We verify that logging the power values homogenises the variances: we get from the overall a variance of the logged power values close to 0.0036, i.e. a reference SD of 0.6 (Figure 13 right).

3-Statistical alert: an example

Logging the power values gives a great simplification of the variance model: the variance is constant so we don't need weighting any more. Exponential or power models can't be fitted anymore. The first statement is that the two estimated thresholds are closer with logged values than with natural values (Figure 14). The second is that the standard deviations (SD) of the residual errors (0.043 and 0.049) are lower than the measured reference SD (0.06). We must be careful with regression witch gives only an adjustment error depending on the number of points. We will use in the following the measured value 0.06 witch is independent of the number of samples. It is important in the following iterative procedure involved. This is another advantage of multiple sampling. We note that the following points (time=17.2 hours) are below the 95% confidence curve (Figure 15 up left). We can't reject that these points belong to the first period. We can include now these points and repeat the procedure for Time<18h. At 21.1h the two points are above the upper confidence curve (Figure 15 up right). Linearity can be rejected with an error risk < 0.1%. We have detected a great variation in the erosion process. Consider now torch 4 (Figure 15 down): because the slope of anode erosion is weaker than for torch 1, there is more difficulty to detect a large departure from the linear trend of the first period. We can reject the linearity only after 31 hours of working with a risk < 5% or after 40h with a lower risk of 0.1%.

4-Confirmation of the piecewise model: another alert procedure

Consider that we observed the next measures and that the rupture of slope seems to be confirmed visually. Because the simple regression (H0 hypothesis) is a special case of the more general piecewise linear model (H1 hypothesis), they can be compared by likelihood ratio test.

Using least squares, this test reduces to F test between two variances. The first one is the difference of fitting of the two models while the second is the residual error of the more general model H1 (cf formulas in $ erosion modelling).

L10_MEAN_POWER For torch 1 and time<25h, p 0 =2, p 1 =4, N=13. We have also: Var (H1-H0) = (0.10771-0.02265)/2 = 0.04753 and Var (H1) = 0.02265/9 = 0.00252. We now calculate the Fischer cumulative function FCF(0.04753/0.00252) with 2 and 9 degrees of freedom. FCF=0.9994 so that the simple model can be rejected with a risk < 0.01%. For torch 4 and time <31.5 we get a result even better.

0 1 0 2 0 3 0 TIME 1, 2 1,3 1,4 1,5 1,6 1,7 1,8 1,9 2,0 2,1
Testing models seems more efficient than prediction. In fact the two tests are not based on the same error: the first is based on the variance measured by multiple sampling, the second on the residual variance estimated by the regression model. Because the measured variance is larger than the estimated one, the first test is conservative and is adequate to detect safely large divergences from the simple linear model.

The tests widely depend on the quality of regression of the first measures, and on the number of these measures. The residual error is much lower for plasma torch 4, may be by good luck! This is not a good way to proceed if we want to elaborate a standardised monitoring. From the two torch examples (Figure 16), it seems essential to proceed to regular measures in the first 16 hours and stay with these values for the following tests. Then the new measures are tested with the confidence limits estimated from the first 16 hours and the fixed measured error from the power spectra. In the current study there are not enough measures in the first period and the second period is worse! It seems reasonable to make at least a measurement per hour during the first 16 hours and twice more after this period of security, when the speed of erosion is capable of a quick increasing.

Torch 1

5-Prediction of the lifetime

We need a limit value tlim for a limit power ylim to be reached. For example, we will hypothesise that the two torches have been stopped because the mean power have exceed the value ylim=1.9 in log10 scale (this hypothesis has to be confirmed by other experiments). In this case, the lifetime is shorter when the slopes of the model are greater. The estimation take place in the second period with slope b, so that we have for the point {ylim, tlim}: tlim=t0+(ylim-y0)/b, and a confidence interval can be computed for tlim.

For torch 1, there are not enough points in the second period: we reach ylim at the same time we are able to confirm the piecewise model. Table 1 The estimated t lim =47 is quite good compared with the whole points estimate 44. We can take its lower confidence value = 42.5 as a limit for a reasonable lifetime. Above 42 hours, the process may be continued with great care, depending on the experience of the operator. This estimation can be repeated each time new values are introduced in the model.

The prediction of the lifetime corresponds to an inverse estimation problem, as for calibration curves. The symmetric Wald confidence interval is not the best one, and more accurate estimates and asymmetrical intervals can be involved with Cook-Weisberg intervals, Monte-Carlo or Bayesian methods. We just want to show here that it is possible.

-Conclusion

The filtering of the signals makes it possible to define two stages of alarm during the working process. The first is given by a threshold model witch detect an important variation of speed in the erosion process. The second is based on an estimation of the lifetime of the torch. These two values separate three periods: a safe period with low speed erosion, a critical period where the erosion progress quickly, and a hazardous period which require a great attention from the operators. All the values estimated or advised must be improved by new experiments in industrial condition of working.

Figure 1 -Figure 2 -Figure 3 -

 123 Figure 1 -Working time in hours versus date for the four plasma torches

FrequencyFigure 4 -

 4 Figure 4 -Sampling and smoothing of plasma torch 4 signals: [a] Sound, [b] Tension. Colors correspond to working times. Note that logging gives similar variability of spectra across time.

Figure 5 -

 5 Figure 5 -Data analysis outline. Statistics are computed from non-filtered and filtered signals, and from their respective smoothed power spectra.

Figure 6 -

 6 Figure 6 -Evolution of the amplitude histograms, computed starting from the original and filtered signals (plasma torch 4, no powder feed)

FILTERINGFigure 7 -

 7 Figure 7 -Evolution of some potential criterions of erosion, computed starting from the original and filtered signals (torch 4, no powder feed)

Figure 8 -

 8 Figure 8 -The effect of filtering on sound spectra. Means and confidence intervals of smoothed powers in function of working time (torch 1 and 4, no powder).

Figure 9 -

 9 Figure 9 -Effect of powder feed on the filtered sound spectra (plasma torch 4).

Figure 10 -

 10 Figure 10 -The effect of filtering on tension spectra. Means and confidence intervals of smoothed powers in function of working time (plasma torch 1 and plasma torch 4, without powder feed).

Figure 11 -

 11 Figure 11 -Repeatability between the two series of measures of tension (with and without powder). Left: torch 4, right: torch 1. The lines connect means and confidence intervals of the smoothed powers.

Figure 12 -

 12 Figure 12 -Modeling of tension power spectra: comparison of exponential, power and piecewise linear models. Top: plasma torch 4 with powder feed. Bottom: Torch 1 without power feed.

Figure 13 -

 13 Figure 13 -Left: relationship between standard deviations and means of the tension spectra (log-log scale). Right: variance of logged data across time

Figure 14 -

 14 Figure 14 -Piecewise linear model with logged data.

L10_MEAN_POWERPlasma

Figure 15 -

 15 Figure 15-Prediction confidence curves for risks 5%, 1% and 0.1% (tension data).

Figure 16 -

 16 Figure 16 -Piecewise model confirmation tests (tension data)

Table 1 -Parameter Estimates for plasma torch 4.

 1 Table 1presents detailed results for torch 4. They compare estimations based on working time < 31.5 with full working time.

			time life < 31.5				whole points
	Parameter	Estimate	Wald 95% Conf. Interval	Estimate	Wald 95% Conf. Interval
				Lower	Upper			Lower	Upper
	A	0,00260	0,00015	0,00504	0,00208 -0,00247	0,00663
	T0	17,42750 14,60186	20,25314	18,76572 14,07377	23,45767
	Y0	1,45833	1,42649	1,49017	1,45482	1,39986	1,50978
	B	0,01492	0,01214	0,01770	0,01769	0,01475	0,02062
	TLIM	47,03626 42,55865	51,51387	43,93431 42,12269	45,74593

  Life Memorial : in memory of David RIGOT died on October 3 rd 2007 Guy DELLUC died on December 15 th 2003 Guy TREILLARD died on August 29 th 1997

	Nomenclature
	H0	Null hypothesis
	H1	Alternate hypothesis
	FCF	Fisher cumulative function
	FFT	Fast Fourier Transform
	h	Frequency [Hertz]
	NCF
	NW	Nadaraya-Watson estimator
	SD	Standard deviation
	SDP	Standard deviation of prediction
	SCF	Student cumulative function
	Var(x)	Variance of variable x
	UCL	Upper confidence limit
	Subscripts
	i	index

Acknowledgements to Jan WIGREN and Volvo Aero Corporation.