Selection of sensors by a new methodology coupling a classification technique and entropy criteria

Complex industrial processes invest a lot of money in sensors and automation devices to monitor and supervise the process in order to guarantee the production quality and the plant and operators safety. Fault detection is one of the multiple tasks of process monitoring and it critically depends on the sensors that measure the significant process variables. Nevertheless, most of the works on fault detection and diagnosis found in literature emphasis more on developing procedures to perform diagnosis given a set of sensors, and less on determining the actual location of sensors for efficient identification of faults. A methodology based on learning and classification techniques and on the information quantity measured by the Entropy concept, is proposed in order to address the problem of sensor location for fault identification. The proposed methodology has been applied to a continuous intensified reactor, the "Open Plate Reactor (OPR)", developed by Alfa Laval and studied at the Laboratory of Chemical Engineering of Toulouse. The different steps of the methodology are explained through its application to the carrying out of an exothermic reaction

Surveillance de procédés à base de méthodes de classification : conception d'un outil d'aide pour la détection et le diagnostic des défaillances

The present work belongs to the field of decision support systems for complex process monitoring, such as chemical and petrochemical plants. Since it is not always possible to obtain a mathematical model for these processes, it is necessary to consider other approaches such as learning and classification methods, in order to identify their different operating modes (normal or faulty). We propose a strategy based on Data Mining techniques, which allows the construction of a discrete event model of the process behavior using historical and online data. This strategy consists on an offline learning stage for the elaboration of a first reference model. This model, in the form of a finite state automaton, must be validated and completed by the process expert. A second online stage consists in tracking the identified process states. A deviation is detected when a given number of observations are not recognized into any expected functional state. At this stage a new learning procedure is proposed in order to identify the nature of the deviation. The new resulting classes and information concerning the descriptors involved are presented to the expert as support for his diagnosis. A decision support software tool for monitoring processes using LAMDA classification algorithm has been developed based on the proposed strategy. LAMDA method uses Fuzzy logic theory and introduces the adequacy concept for the assignment of an element to a class. Within the context of CHEM European project the principal aspects of our work were tested on different industrial and pilot plants.Les travaux présentés se situent dans le domaine de l'aide à la décision pour la surveillance de systèmes complexes tels que les procédés chimiques. Pour de tels procédés il n'est pas toujours possible de disposer d'un modèle mathématique ou structurel du système considéré. De ce fait, d'autres types d'approches telles que les méthodes de classification, doivent être envisagées pour l'identification des états fonctionnels dans lesquels le système peut se trouver. Sur la base de telles méthodes notre travail présente une stratégie permettant de construire, à partir de données historiques et de données récupérées en ligne, un modèle discret (états/transitions) du comportement du processus et d'identifier des situations anormales issues des dysfonctionnements. Cette stratégie consiste à générer un premier modèle de référence, sous la forme d'un automate à états finis, du procédé à partir d'un apprentissage, supervisé ou non. Ce modèle est ensuite validé et complété par l'expert. La reconnaissance en ligne permet de suivre l'évolution temporelle des modes de fonctionnement déjà identifiés. Dans le cas où une transition amène à la non-reconnaissance d'un certain nombre d'éléments, c'est-à-dire à la détection d'une déviation par rapport à un comportement connu, l'objectif est de caractériser cette nouvelle situation. Pour cela, nous proposons de faire un nouvel apprentissage hors ligne prenant en compte ces éléments non reconnus. Les nouvelles classes créées permettent, toujours en interaction avec l'expert, de fixer la nature de la déviation observée. Dans le cas d'une défaillance, une analyse portant sur les descripteurs et le profil des classes permet l'isolation de la défaillance. Ces informations sont transmises à l'opérateur pour l'assister dans son diagnostic. Un outil d'aide à la décision pour la surveillance s'appuyant sur cette stratégie a été mis en place. Cet outil appelé SALSA repose sur la méthode LAMDA. Il s'agit d'une méthode de classification avec apprent issage et reconnaissance de formes qui permet l'analyse de données multi-variables et qui utilise des notions de la logique floue pour introduire le concept d'adéquation d'un élément à une classe. Dans le cadre du projet européen CHEM les principaux aspects de nos travaux et les résultats obtenus ont été illustrés sur des unités industrielles de nature différente

Surveillance de procédés à base de méthodes de classification (conception d'un outil d'aide pour la détection et le diagnostic des défaillances)

Les travaux présentés se situent dans le domaine de l'aide à la décision pour la surveillance de systèmes complexes tels que les procédés chimiques. Sur la base de méthodes pour l'interprétation de données (Data Mining), notre travail présente une stratégie permettant de construire, à partir de données historiques et de données récupérées en ligne, un modèle discret (automate à états finis) du comportement du processus et d'identifier des situations anormales issues des dysfonctionnements. Cette stratégie consiste à générer un premier modèle de référence à partir d'un apprentissage et d'un dialogue avec l'expert. La reconnaissance en ligne permet de suivre l'évolution temporelle des modes de fonctionnement déjà identifiés. Dans le cas de la détection d'une déviation (éléments non reconnus) par rapport à un comportement connu, l'objectif est de caractériser cette nouvelle situation. Pour cela, nous proposons de faire un nouvel apprentissage hors ligne. Les nouvelles classes créées permettent, toujours en interaction avec l'expert, de fixer la nature de la déviation observée. Une analyse portant sur les descripteurs et le profil des classes permet l'isolation d'une éventuelle défaillance. Ces informations sont transmises à l'opérateur pour l'assister dans son diagnostic. Un outil d'aide à la décision pour la surveillance s'appuyant sur cette stratégie a été mis en place. L'outil repose sur la méthode de classification LAMDA, qui permet l'analyse de données multi-variables et qui utilise des notions de la logique floue pour introduire le concept d'adéquation d'un élément à une classe. Les principaux aspects de nos travaux ont été illustrés sur des unités industriellesThe present work belongs to the field of decision support systems for complex process monitoring, such as chemical and petrochemical plants. Since it is not always possible to obtain a mathematical model of the functional states for these processes, it is necessary to consider other approaches such as learning and classification methods, in order to identify their different operating modes (normal or faulty). We propose a strategy, based on Data Mining methods, which allows the construction of a discrete event model of the process behavior using historical and online data. This strategy consists of an offline learning stage for the elaboration of a first reference model. This model, in the form of a finite state automaton, must be validated and completed by the process expert. A second online stage consists in tracking the identified process states. A deviation is detected when a given number of observations are not recognized into any expected functional state. At this stage a new learning procedure is proposed in order to identify the nature of the deviation. The new resulting classes and information concerning the descriptors are presented to the expert as support for his diagnosis. A decision support software tool (SALSA) for monitoring processes using LAMDA classification algorithm has been developed based on the proposed strategy. LAMDA method uses Fuzzy logic theory and introduces the adequacy concept for the assignment of an element to a class. The principal aspects of our work were tested on different industrial and pilot plantsINIST-CNRS (INIST), under shelf-number: RP 17272 / SudocSudocFranceF

Membership-margin based feature selection for mixed type and high-dimensional data: Theory and applications

International audienceThe present paper describes a new feature weighting method based on a membership margin. Distinctive properties of the proposed method include its capability to process problems characterized by mixed-type data (quantitative, qualitative and interval) as well as a huge number of features. The key idea is to map simultaneously all the features of different types into a common space; the membership space. Once all features are represented in a homogeneous space, a feature weighting task can be performed in unified way. This weighting approach is integrated here within a fuzzy classifier through a fuzzy rule weighted concept in order to improve its performance. Each antecedent fuzzy set in the fuzzy if–then rule is weighted to characterize the importance of each proposition and therefore its corresponding feature. Weight estimation process is based on membership margin maximization to estimate a fuzzy weight of each feature in the membership space. Experiments on low and high dimensional real-world datasets demonstrate that the proposed approach can improve significantly the performance of the fuzzy rule-based as well as other state of the art classifiers and can even outperform classical feature weighting approaches. In particular, we show that this approach can yield meaningful results on two real-world applications for cancer prognosis and industrial process diagnosis

Situation prediction based on fuzzy clustering for industrial complex processes

International audiencePrediction of process behavior is important and useful to understand the system status and to take early control actions during operation. This paper presents a fuzzy clustering approach for predicting situations (functional states) in complex process industries. The proposed methodology combines a static measurement, such as the result of a fuzzy classifier trained with historical process data, and an estimation algorithm based on Markov‘s theory for discrete event systems. The situation prediction function is integrated into a process monitoring system without increasing the computational cost, which makes real-time implementation feasible. The monitoring strategy includes two principal stages: an offline stage for designing the fuzzy classifier and the predictor, and an online stage for identifying current process situations and for estimating predicted functional states. Thus, at each sample time, the results of a fuzzy classifier are used as inputs in the prediction procedure. An attractive feature of our proposed method, for situation prediction, is that it provides information about the evolution of the process. The proposed approach was tested on a monitoring system for a power transmission line, and also for monitoring a boiler subsystem of a steam generator. Experimental results indicate that our proposed technique in this paper is effective and can be used as a tool, for operators, to be used in industrial process decision making

Deciphering the role of aquaporins during sperm motility in the marine teleost gilthead seabream

Jornada Anual de la Secció d’Aqüicultura de la Societat Catalana de Biologia, Avenços en Recerca en Aqüicultura, 12 de Juny de 2015, Barcelona.-- 2 pagesIIn the marine teleost gilthead seabream (Sparus aurata) different aquaporin water channels, such as Aqp1aa, -1ab, -7 and -8b, are expressed during the hyperosmotic induction of spermatozoon motility in seawater (SW). However, the physiological roles of these channels are unknown. Single and double immunofluorescence microscopy studies indicate that Aqp1aa and -7 are respectively localized in the entire flagellum or the head, while Aqp1ab and -8b are both in the head and the anterior tail of ejaculated spermatozoa. Upon SW activation, Aqp1ab and -8b are rapidly phosphorylated and translocated to the head plasma membrane and the mitochondrion, respectively, whereas Aqp1aa and -7 remain unchanged. Immunological inhibition of Aqp1aa function reduced the rise of intracellular Ca2+ that normally occurs upon activation, and strongly inhibited sperm motility. Impaired Aqp1aa function also prevented the intracellular trafficking of Aqp8b to the mitochondrion, where it acts as a peroxiporin allowing H2O2 efflux and ATP production during flagellar motility. However, restoring the Ca2+ levels in spermatozoa with immunosuppressed Aqp1aa fully rescued mitochondrial Aqp8b accumulation and sperm motility. In contrast, exposure of sperm to Aqp1ab and -7 antibodies did not affect motility during the initial phase of activation, but latently compromised the trajectory and the pattern of movement. These data reveal the coordinated action of spatially segregated aquaporins during sperm motility activation in seabream, where flagellar-localized Aqp1aa plays a dual Ca2+ -dependent role controlling the initiation of motility and the activation of mitochondrial Aqp8b-mediated detoxification mechanisms, while Aqp1ab and -7 in the head and anterior tail direct the motion pattern. These findings provide new insights into the molecular mechanisms regulating sperm motility in marine teleosts and the causes of male infertility under culture conditionsPeer Reviewe

Symbolic Data Analysis to Defy Low Signal-to-Noise Ratio in Microarray Data for Breast Cancer Prognosis

19International audienceMicroarray profiling has brought recently the hope to gain new insights into breast cancer biology and thereby improve the performance of current prognostic tools. However, it also poses several serious challenges to classical data analysis techniques related to the characteristics of resulted data, mainly high-dimensionality and low signal-to-noise ratio. Despite the tremendous research work performed to handle the first challenge in the feature selection framework, very little attention has been directed to address the second one. We propose in this paper to address both issues simultaneously based on symbolic data analysis capabilities in order to derive more accurate genetic marker-based prognostic models. In particular, interval data representation is employed to model various uncertainties in microarray measurements. A recent feature selection algorithm that handles symbolic interval data is used then to derive a genetic signature. The predictive value of the derived signature is then assessed by following a rigorous experimental setup and compared to existing prognostic approaches in terms of predictive performance and estimated survival probability. It is shown that the derived signature (GenSym) performs significantly better than other prognostic models, including the 70-gene signature, St. Gallen and NIH criterions

Fuzzy logic selection as a new reliable tool to identify molecular grade signatures in breast cancer – the INNODIAG study

International audienceBackground : Personalized medicine has become a priority in breast cancer patient management. In addition to the routinely used clinicopathological characteristics, clinicians will have to face an increasing amount of data derived from tumor molecular profiling. The aims of this study were to develop a new gene selection method based on a fuzzy logic selection and classification algorithm, and to validate the gene signatures obtained on breast cancer patient cohorts.Methods : We analyzed data from four published gene expression datasets for breast carcinomas. We identified the best discriminating genes by comparing molecular expression profiles between histologic grade 1 and 3 tumors for each of the training datasets. The most pertinent probes were selected and used to define fuzzy molecular grade 1-like (good prognosis) and fuzzy molecular grade 3-like (poor prognosis) profiles. To evaluate the prognostic performance of the fuzzy grade signatures in breast cancer tumors, a Kaplan-Meier analysis was conducted to compare the relapse-free survival deduced from histologic grade and fuzzy molecular grade classification.Results : We applied the fuzzy logic selection on breast cancer databases and obtained four new gene signatures. Analysis in the training public sets showed good performance of these gene signatures for grade (sensitivity from 90% to 95%, specificity 67% to 93%). To validate these gene signatures, we designed probes on custom microarrays and tested them on 150 invasive breast carcinomas. Good performance was obtained with an error rate of less than 10%. For one gene signature, among 74 histologic grade 3 and 18 grade 1 tumors, 88 cases (96%) were correctly assigned. Interestingly histologic grade 2 tumors (n = 58) were split in these two molecular grade categories. Conclusion : We confirmed the use of fuzzy logic selection as a new tool to identify gene signatures with good reliability and increased classification power. This method based on artificial intelligence algorithms was successfully applied to breast cancers molecular grade classification allowing histologic grade 2 classification into grade 1 and grade 2 like to improve patients prognosis. It opens the way to further development for identification of new biomarker combinations in other applications such as prediction of treatment response