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The Oncotype DX (ODX) breast cancer assay is the worldwide most common and used Gene Expression Profiling (GEP) test. This ODX assay has a great impact on Adjuvant ChemoTherapy (ACT) decision. However, many standard approaches have been proposed and suggested to practitioners. The accuracy of such methods never reached the highest level. This paper deals with the Breast Cancer Computer Aided Diagnosis (BC-CAD) based on a Deep Constructive Neural Network used for the Recurrence Score (RS) prediction of the ODX assay. The proposed ConstDeepNet algorithm was tested to build two classifiers. In the first architecture, a ”one against all” structure is used where one Deep Neural Network is built for each class. In the second architecture, one DNN is used for the three classes. The proposed BC-CAD algorithm is tested on a real data-set and exhibits good performance. The study data set contains 92 cases carcinoma mammary luminal B with available Oncotype DX test results from 2012 to 2017 taken from the Georges Francois Leclerc cancer centre and the North Trevenans County Hospital located respectively in Dijon and Belfort in France.

Prognostics is a major activity in the field of prognostics and health management. It aims at increasing the reliability and safety of systems while reducing the maintenance cost by providing an estimate of the current health status and remaining useful life (RUL). Classical RUL estimation techniques are usually composed of different steps: estimations of a health indicator, degradation states, a failure threshold, and finally the RUL. In this work, a procedure that is able to estimate the RUL of equipment directly from sensor values without the need for estimating degradation states or a failure threshold is developed. A direct relation between sensor values or health indicators is modeled using a support vector regression. Using this procedure, the RUL can be estimated at any time instant of the degradation process. In addition, an offline wrapper variable selection is applied before training the prediction model. This step has a positive impact on the accuracy of the prediction while reducing the computational time compared to existing indirect RUL prediction methods. To assess the performance of the proposed approach, the Turbofan dataset, widely considered in the literature, is used. Experimental results show that the performance of the proposed method is competitive with other existing approaches.

Breast cancer is the second most common cancer (after lung cancer) that affect women both in the developed and less developed countries. Nowadays, using the Computer Aided Diagnosis (CAD) techniques becomes a necessity for several reasons: assisting and improving physicians, speed in data processing, harmonization and aid of diagnosis, better access to advanced online-medicine. Recently, several works about Breast Cancer Computer Aided Diagnosis (BC-CAD) have been published, and Neural Networks techniques, particularly deep architectures represent a significant part of these works. In this paper, we prpose a BC-CAD based on joint variable selection and a Constructive Deep Neural Network “ConstDeepNet”. A feature variable selection method is applied to decrease the number of inputs used to train a Deep Learning Neural Network. Experiments have been conducted on two datasets, the Wisconsin Breast Cancer Dataset (WBCD) and real data from the north hospital of Belfort (France) to predict the recurrence score of the Oncotype DX. Consequently, the use of joint variable algorithm with ConstDeepNet outperforms the use of the Deep Learning arechitecture alone.

In the Prognostics and Health Management domain, estimating the remaining useful life (RUL) of critical machinery is a challenging task. Various research topics including data acquisition, fusion, diagnostics and prognostics are involved in this domain. This paper presents an approach, based on shapelet extraction, to estimate the RUL of equipment. This approach extracts, in an offline step, discriminative rul-shapelets from an history of run-to-failure data. These rul-shapelets are patterns that are selected for their correlation with the remaining useful life of the equipment. In other words, every selected rul-shapelet conveys its own information about the RUL of the equipment. In an online step, these rul-shapelets are compared to testing units and the ones that match these units are used to estimate their RULs. Therefore, RUL estimation is based on patterns that have been selected for their high correlation with the RUL. This approach is different from classical similarity-based approaches that attempt to match complete testing units (or only late instants of testing units) with training ones to estimate the RUL. The performance of our approach is evaluated on a case study on the remaining useful life estimation of turbofan engines and performance is compared with other similarity-based approaches.

In this work, an effort is made to characterize seven bearing states depending on the energy entropy of Intrinsic Mode Functions (IMFs) resulted from the Empirical Modes Decomposition (EMD). Three run-to-failure bearing vibration signals representing different defects either degraded or different failing components (roller, inner race and outer race) with healthy state lead to seven bearing states under study. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used for feature reduction. Then, six classification scenarios are processed via a Probabilistic Neural Network (PNN) and a Simplified Fuzzy Adaptive Resonance Theory Map (SFAM) neural network. In other words, the three extracted feature data bases (EMD, PCA and LDA features) are processed firstly with SFAM and secondly with a combination of PNN-SFAM. The computation of classification accuracy and scattering criterion for each scenario shows that the EMD-LDA-PNN-SFAM combination is the suitable strategy for online bearing fault diagnosis. The proposed methodology reveals better generalization capability compared to previous works and it is validated by an online bearing fault diagnosis. The proposed strategy can be applied for the decision making of several assets. Display Omitted A new methodology proposed for a nearly online damage stage detection.The proposed strategy is based on neural networks and EMD method.The nearly online detection of bearing health status is done thanks to various damage stages.Experimental results show that this methodology is very effective.Unlike previous works, the proposed method is tested for the diagnosis of naturally progressing bearing degradations.

Condition monitoring and fault diagnosis of rolling element bearings (REBs) are at present very important to ensure the steadiness of industrial and domestic machinery. According to the non-stationary and non-linear characteristics of REB vibration signals, feature extraction method is based on empirical mode decomposition (EMD) energy entropy in this paper. A mathematical analysis to select the most significant intrinsic mode functions (IMFs) is presented. Therefore, the chosen features are used to train an artificial neural network (ANN) to classify bearings defects. Experimental results indicated that the proposed method based on run-to-failure vibration signals can reliably categorize bearing defects. Using a proposed health index (HI), REB degradations are perfectly detected with different defect types and severities. Experimental results consist in continuously evaluating the condition of the monitored bearing and thereby detect online the severity of the defect successfully. This paper shows potential application of ANN as effective tool for automatic bearing performance degradation assessment without human intervention.

International audience; This paper deals with knowledge capitalization in maintenance especially in diagnosis and repair of industrial equipments. The goal is to propose a method of knowledge capitalization in order to develop a decision support system for maintenance operators. The knowledge capitalization cycle was adopted as the underlying principle. It consists of four principal steps: detect, preserve, capitalize and actualize the strategic knowledge. Different knowledge management tools and methods that can be used in the cycle are reviewed. We propose a mix method of knowledge capitalization in maintenance. This method integrates a representation and a reasoning model both completing each other and suitable to represent and manipulate the domain knowledge. The knowledge representation model using unified modelling language (UML) diagram proposes different domain models based on maintenance analysis to guide the domain expertise. The reasoning model uses the case-based reasoning which allows the manipulation of represented domain knowledge. Finally, the method is implemented on the pallet transfer system Sormel in the context of Proteus e-maintenance platform.

International audience; In fault detection systems, massive amount of data gathered from the life-cycle of equipment is often used to learn models or classifiers that aims at diagnosing different kind of errors or failures. Among this huge quantity of information, some features (or sets of features) are more correlated with the kind of failures than others. The presence of irrelevant features might affect the performance of the classifier. To improve the performance of a detection system, feature selection is hence a key step. We propose in this paper an algorithm named STRASS, that aims at detecting relevant features for classification purposes. In certain cases, when there exists a strong correlation between some features and the associated class, classical feature selection algorithms fail at selecting the most relevant features. In order to cope with this problem, STRASS algorithm makes use of k-way correlation between features and the class to select relevant features. To assess the performance of STRASS, we apply it on simulated data collected from the Tennessee Eastman chemical plant simulator.The Tennessee Eastman process (TEP) has been used in many fault detection studies and three specific faults are not well discriminated with classical algorithms. The results obtained by STRASS are compared to those obtained with reference feature selection algorithms. We show that the features selected by STRASS always improve the performance of a classifier compared to the whole set of original features and that the obtained classification is better than with most of the other feature selection algorithms.

Remaining Useful Life (RUL) prediction accurately is an imperative industrial challenge. In this sense, the monitoring of lithium-ion battery is very significant for planning repair work and minimizing unexpected electricity outage. As the RUL estimation is essentially a problem of pattern recognition, the most valuable feature extraction techniques and more accurate classifier are needed to obtain higher prognostic effectiveness. Consequently, this paper discusses the importance of non linear feature reduction techniques for more adequate prognosis feature data base. For more convenience, the isometric feature mapping technique (ISOMAP) is used to reduce some features extracted from lithium-ion batteries, with different health states, in both modes of charge and discharge. Experimental results show that non linear feature reduction techniques are very promising to provide some trend parameters for industrial prognostic.

International audience; Many models and methodologies in order to predictthe remaining useful life (RUL) of a critical component areinvestigated nowadays. However, estimating remaining useful lifeof multi-component systems is still an under explored area,especially when there are interdependencies among the componentsof these systems. Practically, prognostics can be quitecomplicated when there is absence of prior knowledge aboutthese interactions. To optimize the availability and reliabilityof the system, it is required to embed a dependency modelthat implements component strength and the component’s healthstatus. In this paper, a novel approach is proposed in orderto emphasize the importance of interactions between complexsystem’s components in the RUL’s calculating. The effectivenessof the approach is judged by applying it to numerical studies inorder to estimate system’s remaining useful life.

In this paper, a conceptual model of maintenance process is proposed in the aim of releasing different types of help (decision-help systems) to maintenance actors. The maintenance domain is defined and modelled with the aid of knowledge management. The object-oriented representation was chosen in order to propose real domain model for development of information system or prototype. This system should comply with decision support and allow technico-economical knowledge capitalisation.

International audience; Accurate remaining useful life (RUL) prediction of critical assets is an important challenge in condition based maintenance to improve reliability and decrease machine's breakdown and maintenance's cost. Bearing is one of the most important components in industries which need to be monitored and the user should predict its RUL. The challenge of this study is to propose an original feature able to evaluate the health state of bearings and to estimate their RUL by Prognostics and Health Management (PHM) techniques. In this paper, the proposed method is based on the data-driven prognostic approach. The combination of Simplified Fuzzy Adaptive Resonance Theory Map (SFAM) neural network and Weibull distribution (WD) is explored. WD is used just in the training phase to fit measurement and to avoid areas of fluctuation in the time domain. SFAM training process is based on fitted measurements at present and previous inspection time points as input. However, the SFAM testing process is based on real measurements at present and previous inspections. Thanks to the fuzzy learning process, SFAM has an important ability and a good performance to learn nonlinear time series. As output, seven classes are defined; healthy bearing and six states for bearing degradation. In order to find the optimal RUL prediction, a smoothing phase is proposed in this paper. Experimental results show that the proposed method can reliably predict the RUL of rolling element bearings (REBs) based on vibration signals. The proposed prediction approach can be applied to prognostic other various mechanical assets.

Case base maintenance.Prototyping.Instance reduction.Competence and performance optimization.Structuring and updating a case base. To avoid performance degradation and maintain the quality of results obtained by the case-based reasoning (CBR) systems, maintenance becomes necessary, especially for those systems designed to operate over long periods and which must handle large numbers of cases. CBR systems cannot be preserved without scanning the case base. For this reason, the latter must undergo maintenance operations.The techniques of case base's dimension optimization is the analog of instance reduction size methodology (in the machine learning community). This study links these techniques by presenting case-based maintenance in the framework of instance based reduction, and provides: first an overview of CBM studies, second, a novel method of structuring and updating the case base and finally an application of industrial case is presented.The structuring combines a categorization algorithm with a measure of competence CM based on competence and performance criteria. Since the case base must progress over time through the addition of new cases, an auto-increment algorithm is installed in order to dynamically ensure the structuring and the quality of a case base. The proposed method was evaluated through a case base from an industrial plant. In addition, an experimental study of the competence and the performance was undertaken on reference benchmarks. This study showed that the proposed method gives better results than the best methods currently found in the literature.

International audience; Thanks to ICT, Web emergency and Internet, the achievement of maintenance services and monitoring can be performed automatically, remotely and through various distributed information systems. Hence the emergence of the concept of services offered through maintenance architectures, ranging from autonomic systems to integrated systems where knowledge management, cooperation and collaboration are vital to any operation. Into this context, new services like intelligent maintenance, self maintenance, etc are required. To this end, a new concept called s-maintenance is emerged. This concept defines a new generation of maintenance systems founded on a knowledge based system. While existing systems don't respond to the characteristics of this new generation of systems, we design in this paper an architecture of a maintenance component based system respecting the characteristics of s-maintenance. Each component in the system is defined to respond to one or many characteristics of this concept.

Accurate Remaining Useful Life (RUL) prediction of critical assets is an important challenge in condition based maintenance to improve reliability and to decrease machine's breakdown and maintenance's cost. Bearing is one of the most important components in industries that need to be monitored and the user should predict its RUL. The challenge of this study is to propose a new strategy for RUL feature extraction. The proposed methodology provides better features in term of monotonicity. This specification ensures a better RUL prediction by comparing the test degradation features to the library of instance. Experimental results show that the proposed methodology is very promising for RUL estimation by industry.

Bearings are important assets for most industrial applications. The non-destructive diagnosis of these elements needs an accurate and reliable acquisition of its dynamic vibration signals affected by noise and the other part of system such as gears, shafts, etc. Empirical mode decomposition is an advanced signal processing tool for bearing fault feature extraction. In this paper, empirical mode decomposition is used to decompose non-linear and non-stationary bearing vibration signals into several stationary intrinsic mode functions and the empirical mode decomposition energy entropy is computed for each intrinsic mode function. Moreover, principal component analysis and linear discriminant analysis are used for feature reduction. Based on the Fisher's criterion, experimental results show that linear discriminant analysis features are highlighted compared to principal component analysis features and original empirical mode decomposition features for bearing fault diagnosis as type (inner race, outer race, rolling element) and severity (normal, degraded, faulting).

International audience; In the Prognostics and Health Management domain, estimating the remaining useful life (RUL) of critical machinery is a challenging task. Various research topics as data acquisition and processing, fusion, diagnostics, prognostivs and decision are involved in this domain. This paper presents an approach for estimating the Remaining Useful Life (RUL) of equipments based on shapelet extraction and characterization. This approach makes use in a first step of an history of run-to-failure data to extract discriminative rul-shapelets, i.e. shapelets that are correlated with the RUL of the considered equipment. A library of rul-shapelets is extracted from this step. Then, in an online step, these rul-shapelets are compared to different test units and the ones that match these units are used to estimate their RULs. This approach is hence different from classical similarity-based approaches that matches the test units with training ones. Here, discriminative patterns from the training set are first extracted and then matched to test units. The performance of our approach is assessed on a data set coming from a previous PHM Challenge. We show that this approach is efficient to estimate the RUL compared to other approaches.

International audience; Prognostics is a major activity of Condition-Based Maintenance (CBM) in many industrial domains where safety,reliability and cost reduction are of high importance. The main objective of prognostics is to provide an estimation of the Remaining Useful Life (RUL) of a degrading component/ system, i.e. to predict the time after which a component/system will no longer be able to meet its operating requirements. RUL prediction is a challenging task that requires special attention when modeling the prognostics approach. This paper proposes a RUL prediction approach based on Instance Based Learning (IBL) with an emphasis on the retrieval step of the latter. The method is divided into two steps: an offline and an online step.The purpose of the offline phase is to learn a model that represents the degradation behavior of a critical component using a history of run-to-failure data. This modeling step enablesus to construct a library of health indicators (HI) from run-to-failure data. These HI’s are then used online to estimate the RUL of components at an early stage of life, by comparing their HI’s to the ones of the library built in the offline phase. Our approach makes use of a new similarity measure between HIs. The proposed approach was tested on real turbofan data set and showed good performance compared to other existing approaches.

In the most of industrial and domestic applications bearings present important assets. The diagnostic of these elements needs accurate and reliable acquisition of its dynamic vibration signals affected by noise and other part of system such as gears, bars... Empirical Mode Decomposition (EMD) is a new signal processing method used to decompose non-stationary and non-linear vibration bearing signals into several stationary empirical mode components called Intrinsic Mode Functions (IMF). For each IMF, the energy entropy mean is computed. This technique is compared to the most used statistical features (RMS, Kurtosis) using a characterization degree. Experimental results show that time domain feature extraction is effective for bearing fault feature extraction as type (inner race, outer race, rolling element) and severity (normal, degraded, faulting). The choice of the most significant IMFs is also discussed in this paper.

International audience; In case of a radiological emergency situation involving accidental human exposure, a dosimetry evaluation must be established as soon as possible. In most cases, this evaluation is based on numerical representations and models of victims. Unfortunately, personalised and realistic human representations are often unavailable for the exposed subjects. However, accuracy of treatment depends on the similarity of the phantom to the victim. The EquiVox platform (Research of Equivalent Voxel phantom) developed in this study uses Case-Based Reasoning (CBR) principles to retrieve and adapt, from among a set of existing phantoms, the one to represent the victim. This paper introduces the EquiVox platform and the Artificial Neural Network (ANN) developed to interpolate the victim's 3D lung contours. The results obtained for the choice and construction of the contours are presented and discussed.

International audience; Prognostics and Health Management platforms are founded on Condition Based Maintenance process. Major works on this topic are focused on the diagnostic and prognostic modules and neglect the Decision Support Module which must be investigated to give more efficiency to PHM platforms. To improve this module with intelligence and rapidity we propose in this work to integrate a Trace Based System (TBS) into the decision support module. This TBS provides three main services (Traceability, self-learning and self-management) which are developed in this work.

International audience; In case of a radiological emergency situation involving accidental human exposure, a dosimetry evaluation must be established as soon as possible. In most cases, this evaluation is based on numerical representations and models of subjects. Unfortunately, personalised and realistic human representations are often unavailable for the exposed subjects. However, accuracy of treatment depends on the similarity of the phantom to the subject. The EquiVox platform (Research of Equivalent Voxel phantom) developed in this study uses Case-Based Reasoning principles to retrieve and adapt, from among a set of existing phantoms, the one to represent the subject. This paper introduces the EquiVox platform and Artificial Neural Networks developed to interpolate the subject’s 3D lung contours. The results obtained for the choice and construction of the contours are presented and discussed.

In the case of a radiological emergency situation, involving accidental human exposure, it is necessary to establish as soon as possible a dosimetry evaluation. In most cases, this evaluation is based on numerical representations and models of the victims. Unfortunately, personalised and realistic human representations are often unavailable for the exposed subjects. Hence, existing models like the 'Reference Man' representative of the average male individual are used. However, the accuracy of the treatment depends on the similarity of the phantom to the victim. The EquiVox platform (Research of Equivalent Voxel phantom) developed in this work uses the case-based reasoning principles to retrieve, from a set of existing phantoms, the most adapted one to represent the victim. This paper introduces the EquiVox platform and gives the example of in vivo lung monitoring optimisation to prove its efficiency in choosing the right model. It also presents the artificial neural network tools being developed to adapt the model to the victim.

International audience; Platforms expand maintenance systems from centralized systems into e-maintenance platforms integrating various cooperative distributed systems and maintenance applications. This phenomenon allowed an evolution in services offered to maintenance actors by integrating more intelligent applications, providing decision support and facilitating the access to needed data. To manage this evolution, e-maintenance platforms must respond to a great challenge which is ensuring an interoperable communication between its integrated systems. By combining different techniques used in previous works, we propose in this work a semantic mediator system ensuring a high level of interoperability between systems in the maintenance platform.

International audience; Maintenance is becoming more and more crucial in Asset Lifecycle Management information models. Issues such as collecting, handling and using the asset data produced during its lifecycle in a lean and efficient manner are on top of today's research. Customer satisfaction, compliance with environmental friendly legislation, product quality, high performance and reliability are only a few of the benefits improved maintenance methods and tools may provide to enterprises. In this work we combine the benefits of two previous developed models and we develop a model for Semantic Maintenance. The first model we are based on is the PROMISE semantic object model which was made for supporting Closed-Loop Product Lifecycle Management. The second model is the semantic model of e-maintenance developed in PROTEUS project. The new model described in this paper is named “SMAC-Model”. Its aim is to provide advanced maintenance services as well as feedback for the Beginning of Life and input for End of Life. The model is generic and may be used in various Asset Lifecycle Management cases. It is developed to facilitate complex physical assets and to work in industrial environment.

International audience; In case of accidental exposure to radiation, it is necessary to establish as soon as possible a dosimetry report for each victim. In most cases, this report is based on medical images of the victim, enabling the construction of a personalized realistic numerical model, also called a voxel phantom. Unfortunately it is not always possible to perform the medical imaging of the victim since the technology may be unavailable or to avoid additional exposure to radiation. In such cases, the commonly used method is to represent the victim with a numerical model like the "Reference Man", a voxelized phantom representative of the average male individual. The treatment accuracy depends on the diagnosis precision and, consequently, on the similarity of the phantom and/to the victim. A precise dosimetry evaluation requires a personalised and realistic phantom whose biometric characteristics match the victim; such model is often unavailable. The Case-Based Reasoning (CBR) is a problem solving method for the conception of intelligent systems. It imitates the analysis, understanding and reconstruction of the human intelligence. The ReEPh project (Research of Equivalent Phantom) proposes to use the CBR principles to retrieve from a set of phantoms, the most adapted one to the irradiated victim. For this study, the ReEPh platform retrieves, stores and compares existing phantoms to a victim. A graphic interface enables the user to compare victim's characteristics to the ones of the most similar phantoms available in the database. This defines a similarity index presenting the equivalence between the victim and the suggested phantom. Moreover, a confidence index is also assessed to define the uncertainty implied by the RaPC choice procedure. © EDP Sciences, 2010.

International audience; The objective of this paper is to address the diagnosis knowledge-oriented system in terms of artificial intelligence, particular by the Case-Based Reasoning (CBR) approach. Indeed, the use of CBR, which is an approach to problem solving and learning, in diagnosis goes back to a long time with the appearance of diagnostic support systems based on CBR. A diagnostic system by CBR implements an expertise-base composed of past experiences through which the origins of failure and the maintenance strategy are given according to a description of a specific situation of diagnostic. A study is made on the different diagnostic systems based on CBR. This study showed that there was no common methodology for building a CBR system. This design depends primarily on the case representation and knowledge models of the domain application. Consequently, this paper proposes a general design approach of a diagnostic system based on the CBR approach.

International audience; Technological and software progress with the evolution of processes within company have highlighted the need to evolve systems of maintenance process from autonomous systems to cooperative and sharing information system based on software platform. However, this need gives rise to various maintenance platforms. The first part of this study investigates the different types of existing industrial platforms and characterizes them compared to two criteria namely : information exchange and relationship intensity. This allowed identifying the e-maintenance architecture as the current most efficient architecture. despite its effectiveness, this latter can only guarantee technical interoperability between various components. Therefore, the second part of this study proposes a semantic-knowledge based architecture, thereby ensuring a higher level of semantic interoperability. To this end, specific maintenance ontology has been developed.

International audience; We have developed a diagnostic help system dedicated to the maintenance of a supervised industrial system for pallets Transfer (SISTRE). This diagnostic help system is based on a Case-Based Reasoning approach (CBR). The expertise considered in this help system and formalized in the case form in a case-base must be updated, while taking account of its quality. In this objective we propose a method allowing on one hand to structure the case-base and on the other hand to auto-increment it. An experimental study is undertaken through references benchmarks as well as an application on SISTRE.

International audience; In the domain of radiation protection it is not always possible to perform an additional examination such as scanners or Magnetic Resonance Imaging (MRI) for a patient who has been accidentally radiated. However a medical diagnostic must be made as soon as possible to calculate the dosimetric balance. Currently this incidental calculation is based on the available voxelised phantoms which are 3D numerical reconstructions of the human body with the internal organs. The Case-Based Reasoning (CBR) is seen on the one hand like a problem solving method and on the other hand like a technology for the conception of intelligent systems. The ReEPh project (Research of Equivalent Phantom) strikes a new path in the field of problem solving methods in the radiation protection and uses the approach of the CBR to retrieve the set of the phantoms the most adapted to the irradiated victim. For this first version of ReEPh, the retrieval phase uses a Knn Algorithm (K nearest neighbours). We propose a measure of similarity and a confidence index to take into account the uncertainty implied by the possible missing characteristics of the victim. We have developed a graphic interface to view the cases retrieval, and to visually illustrate the combination of measures of similarity and confidence index.

International audience; Conditions complexity of management workshop of production manufacturing increases and requires heuristic algorithm adapted to the context. The principal difficulty then lies in the choice of heuristic algorithm to apply. We propose a help method to schedule, and more specifically a parameter setting up's help of an industrial scheduling software, being based on machine learning system able to extract knowledge from data. An inductive learning based on examples system is developed and replaced in a process of ECD (Extraction of Knowledge starting from Data). The first step pf this process is specific to our problem and uses in this case the capacities of simulation of a market software of scheduling.