Scheduling predictive maintenance in flow-shop.

International audienceAvailability of production equipments is one major issue for manufacturers. Predictive maintenance is an answer to prevent equipment from risk of breakdowns while minimizing the maintenance costs. Nevertheless, conflicts could occur between maintenance and production if a maintenance operation is programmed when equipment is used for production. The case studied here is a flow-shop typology where machines could be maintained once during the planning horizon. Machines are able to switch between two production modes. A nominal one and a degraded one where machine run slowly but increase its remaining useful life. We propose a mixed integer programming model for this problem with the makespan and maintenance delays objective. It allows to find the best schedule of production operation. It also produces, for each machine, the control mode and if necessary the preventive maintenance plan

Residual-based failure prognostic in dynamic systems.

International audienceThis paper deals with failure prognostic in dynamic systems. The system's remaining useful life is estimated based on residual signals. This supposes the possibility to build a dynamic model of the system by using the bond graph tool, and the existence of a degradation model in order to predict its future health state. The choice of bond graph is motivated by the fact that it is well suited for modeling physical systems where several types of energies are involved. In addition, it allows to generate residuals for fault diagnostic and prognostic. The proposed method is then applied on a simple dynamic model of a hydraulic system to show its feasibility

Hybrid prognostic method applied to mechatronic systems.

International audienceFault detection and isolation, or fault diagnostic, of mechatronic systems has been subject of several interesting works. Detecting and isolating faults may be convenient for some applications where the fault does not have severe consequences on humans as well as on the environment. However, in some situations, diagnosing faults may not be sufficient and one needs to anticipate the fault. This is what is done by fault prognostics. This latter activity aims at estimating the remaining useful life of systems by using three main approaches: data-driven prognostics, model-based prognostics and hybrid prognostics. In this paper, a hybrid prognostic method is proposed and applied on a mechatronic system. The method relies on two phases: an offline phase to build the behavior and degradation models and an online phase to assess the health state of the system and predict its remaining useful life

Pronostic de défaillances : Maîtrise de l'erreur de prédiction.

International audienceLe travail rapporté ici traite globalement de la spécification et du développement d'un système de pronostic de défaillances. De ce point de vue, beaucoup de développements visant la proposition de méthodes de prévision existent dans la littérature. La majorité d'entre elles portent sur la construction de modèles capables de minimiser l'erreur de prédiction d'une situation future. Cependant, peu traitent de la maitrise de cette erreur. C'est ce qui fait l'objet de ce papier et pour lequel nous proposons d'exploiter le système ANFIS (système d'inférence floue paramétré par apprentissage neuronal). Après avoir positionné l'activité de pronostic dans le cadre de la maintenance industrielle, nous présentons le réseau ANFIS. Nous étudions les pistes permettant de maîtriser l'erreur de prédiction d'un tel système, notamment lors de la phase d'apprentissage (optimisation des paramètres du réseau). Les éléments théoriques nécessaires à cette analyse sont décrits, une nouvelle fonction de coût est proposée et l'influence de celle-ci sur les performances du réseau est discutée. Nous illustrons l'ensemble sur un benchmark. La modification proposée permet de réduire la phase d'apprentissage du système de pronostic

Pronostic industriel : étude de l'erreur de prédiction du système ANFIS.

International audienceLe travail porte globalement sur le développement d'un outil de pronostic de défaillances basé sur l'utilisation d'un système de prédiction neuro-flou. Plus particulièrement, cet article vise la proposition d'une architecture de prédiction basée sur l'utilisation du système ANFIS (système d'inférence floue paramétré par apprentissage neuronal), et pour laquelle différents axes d'améliorations des prédictions sont proposés. La stabilité des erreurs de prédictions en fonction de l'horizon de prédiction est étudiée expérimentalement et une solution visant à intégrer les sollicitations "futures" connues dans le modèle prédictif est proposée. L'ensemble est illustré sur un benchmark de prédiction : la série de données dite de Box-Jenkins

SW-ELM : A summation wavelet extreme learning machine algorithm with a priori initialization.

International audienceCombining neural networks and wavelet theory as an approximation or prediction models appears to be an effective solution in many applicative areas. However, when building such systems, one has to face parsimony problem, i.e., to look for a compromise between the complexity of the learning phase and accuracy performances. Following that, the aim of this paper is to propose a new structure of connectionist network, the Summation Wavelet Extreme Learning Machine (SW-ELM) that enables good accuracy and generalization performances, while limiting the learning time and reducing the impact of random initialization procedure. SW-ELM is based on Extreme Learning Machine (ELM) algorithm for fast batch learning, but with dual activation functions in the hidden layer nodes. This enhances dealing with non-linearity in an efficient manner. The initialization phase of wavelets (of hidden nodes) and neural network parameters (of input-hidden layer) is performed a priori, even before data are presented to the model. The whole proposition is illustrated and discussed by performing tests on three issues related to time-series application: an "input-output" approximation problem, a one-step ahead prediction problem, and a multi-steps ahead prediction problem. Performances of SW-ELM are benchmarked with ELM, Levenberg Marquardt algorithm for Single Layer Feed Forward Network (SLFN) and ELMAN network on six industrial data sets. Results show the significance of performances achieved by SW-ELM

Novel failure prognostics approach with dynamic thresholds for machine degradation.

International audienceEstimating remaining useful life (RUL) of critical machinery is a challenging task. It is achieved through essential steps of data acquisition, data pre-processing and prognostics modeling. To estimate RUL of a degrading machinery, prognostics modeling phase requires precise knowledge about failure threshold (FT) (or failure definition). Practically, degrading machinery can have different levels (states) of degradation before failure, and prognostics can be quite complicated or even impossible when there is absence of prior knowledge about actual states of degrading machinery or FT. In this paper a novel approach is proposed to improve failure prognostics. In brief, the proposed prognostics model integrates two new algorithms, namely, a Summation Wavelet Extreme Learning Machine (SWELM) and Subtractive-Maximum Entropy Fuzzy Clustering (S-MEFC) to predict degrading behavior, automatically identify the states of degrading machinery, and to dynamically assign FT. Indeed, for practical reasons there is no interest in assuming FT for RUL estimation. The effectiveness of the approach is judged by applying it to real dataset in order to estimate future breakdown of a real machinery

Dynamic scheduling of maintenance activities under uncertainties.

International audienceCompetencies management in the industry is one of the most important keys in order to obtain good performance with production means. Especially in maintenance services field where the dierent practical knowledges or skills are their working tools. We address, in this paper, the both assignment and scheduling problem that can be found in a maintenance service. Each task that has to be performed is characterized by a competence level required. Then, the decision problem of assignment and scheduling lead to find the good resource and the good time to do the task. For human resources, all competence levels are dierent, they are considered as unrelated parallel machines. Our aim is to assign dynamically new tasks to the adequate resources by giving to the maintenance expert a choice between the robustest possibilities

Joint prediction of observations and states in time-series based on belief functions

International audienceForecasting the future states of a complex system is a complicated challenge that is encountered in many industrial applications covered in the community of Prognostics and Health Management (PHM). Practically, states can be either continuous or discrete: Continuous states generally represent the value of a signal while discrete states generally depict functioning modes reflecting the current degradation. For each case, specific techniques exist. In this paper, we propose an approach based on case-based reasoning that jointly estimates the future values of the continuous signal and the future discrete modes. The main characteristics of the proposed approach are the following: 1) It relies on the K-nearest neighbours algorithm based on belief functions theory; 2) Belief functions allow the user to represent his partial knowledge concerning the possible states in the training dataset, in particular concerning transitions between functioning modes which are imprecisely known; 3) Two distinct strategies are proposed for states prediction and the fusion of both strategies is also considered. Two real datasets were used in order to assess the performance in estimating future break-down of a real system

Static et dynamic scheduling of maintenance activities under the constraints of skills.

International audienceSkill management in industry is one of the most important factors required in order to obtain optimal performance of the production system. This is of particular importance in the field of maintenance where the different practical knowledge or skills are the working tools used. We address, in this paper, both the assignment and scheduling problems that may be found in a maintenance service. Each task that has to be performed is characterized by the level of skill required. The problem lies with making the decision of which time is the right time for the assignment and scheduling of the correct resource to do the task. We introduce both static and dynamic scheduling, applied to the maintenance task assignment. To confer a maximum robustness to the obtained schedule, tha approach proposed in this paper is completed by a proactive methodology which takes into account possible variations