Asynchronous Stochastic Variational Inference

Stochastic variational inference (SVI) employs stochastic optimization to scale up Bayesian computation to massive data. Since SVI is at its core a stochastic gradient-based algorithm, horizontal parallelism can be harnessed to allow larger scale inference. We propose a lock-free parallel implementation for SVI which allows distributed computations over multiple slaves in an asynchronous style. We show that our implementation leads to linear speed-up while guaranteeing an asymptotic ergodic convergence rate $O(1/\sqrt(T)$ ) given that the number of slaves is bounded by $\sqrt(T)$ ($T$ is the total number of iterations). The implementation is done in a high-performance computing (HPC) environment using message passing interface (MPI) for python (MPI4py). The extensive empirical evaluation shows that our parallel SVI is lossless, performing comparably well to its counterpart serial SVI with linear speed-up.Comment: 7 pages, 8 figures, 1 table, 2 algorithms, The paper has been submitted for publicatio

Discrete Event Model-Based Approach for Fault Detection and Isolation of Manufacturing Systems

International audienceThis paper presents a discrete event model-based approach for Fault Detection and Isolation of manufacturing systems. This approach considers a system as a set of independent plant elements. Each plant element is composed of a set of interrelated Parts of Plant (PoPs) modeled by a Moore automaton. Each PoP model is only aware of its local behavior. The degraded and faulty behaviors are added to each PoP model in order to obtain extended PoP ones. An extrapolation of Gaussian learning is realized to obtain acceptable temporal intervals between the time occurrences of correlated events. Finally based on the PoP extended models and the links between them, a fault candidates' tree is established for each plant element. This candidates' tree corresponds to a local on-line fault event occurrence observer, called diagnoser. Thus, the diagnosis decision is distributed on each plant element. An application example is used to illustrate the approach

Automatic control to improve the seaworthiness conditions in inland navigation

This paper focuses on the Normal Navigation Level (NNL) control of a hydraulic channel that is located in the northwest of France and belongs to the Europe Inland Navigation Network. This system is a large scale system with several inputs and outputs that nowadays is operated manually and with local controllers that try to maintain the level of the channel as close as possible to the NNL and fulfils the seaworthiness requirements. For recent years, the channel has been equipped with electronic sensors in order to have better knowledge of its behaviour, provide online the state to the lockkeepers and improve its management. In this work, an automatic control based on a Model Predictive Controller (MPC) is proposed. The MPC controller is based on a model of the system and, with the available data, provides automatically the suitable control inputs (flows) in order to maintain the level in all the points of the channel despite the locks operation that produces wave phenomena and other unknown inputs along the channel. A numerical simulator of the system based on the Saint-Venant Equations and calibrated with real data has been developed in order to verify the effectiveness of the proposed automatic controller.Peer ReviewedPostprint (author’s final draft

Explainable Predictive Maintenance

Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research.Comment: 51 pages, 9 figure

Conception d'un système de diagnostic adaptatif et prédictif basé sur la méthode Fuzzy Pattern Matching pour la surveillance en ligne des systèmes évolutifs

Sylviane GENTIL, Patrick MILLOT, Didier MAQUIN, Janan ZAYTOONAn industrial process works under two operating states : normal and abnormal one. A set of measurements about these operating states forms the training set, which is divided into classes. Each class is associated with an operating state. The purpose of the diagnosis by pattern recognition is to assign, at each instant, a new measurement to one of the known classes. Fuzzy Pattern Matching (FPM) is a classification method, which uses the fuzzy sets and the possibility theory to take into account the imprecision and the uncertainty of the data. Generally, the number of training samples is not enough to show all the functioning modes of a system. Moreover, predicting the evolution of the functioning mode from normal to abnormal one is useful to avoid the bad consequences. Additionally, in a non-stationary work environment, a classifier must be retrained every time a new sample is classified, to obtain a new knowledge from it. This is an impractical solution since it requires the storage of all available samples and a considerable computation time. The solution is the development of a classifier that is capable to acquire new knowledge from each new classified sample while preserving the current one, this solution is known as incremental learning.The objective of this thesis is to develop FPM to be capable to realize an adaptive and predictive diagnosis in real time and in a non stationary work environment.La supervision automatique des processus industriels permet d'accroître la productivité et de diminuer le coût d'entretien. Le diagnostic est une composante principale d'un module de supervision. Il existe plusieurs approches pour réaliser le diagnostic. Les performances de chaque approche dépendent du problème posé. Nous cherchons une méthode de diagnostic capable de résoudre les problèmes suivants :- dans une base de connaissance incomplète, tous les modes de fonctionnement ne sont pas représentés. En conséquence, un module de diagnostic doit être adaptatif afin d'inclure à sa base de connaissance les nouveaux modes dés qu'ils apparaissent,- lorsque le système évolue vers un mode anormal ou non désiré, il est nécessaire d'anticiper cette évolution plutôt que d'attendre d'arriver à ce mode afin d'éviter ses conséquences surtout s'il est dangereux. Le module de diagnostic doit donc être prédictif,- dans le cas d'un système évolutif, la base de connaissance doit être enrichie grâce à l'information apportée par les nouvelles observations. Cet enrichissement doit être réalisé en temps réel,- les données sont à la fois incertaines et imprécises.L'objectif principal de ma thèse consistait à mettre au point un module de diagnostic en temps réel adaptatif et prédictif pour des systèmes évolutifs, en utilisant les techniques de Reconnaissance des Formes, la théorie des ensembles flous et la théorie des possibilités. Ce module a été appliqué sur plusieurs applications industrielles

Conception d'un système de diagnostic adaptatif et prédictif basé sur la méthode Fuzzy Pattern Matching pour la surveillance en ligne des systèmes évolutifs

Sylviane GENTIL, Patrick MILLOT, Didier MAQUIN, Janan ZAYTOONAn industrial process works under two operating states : normal and abnormal one. A set of measurements about these operating states forms the training set, which is divided into classes. Each class is associated with an operating state. The purpose of the diagnosis by pattern recognition is to assign, at each instant, a new measurement to one of the known classes. Fuzzy Pattern Matching (FPM) is a classification method, which uses the fuzzy sets and the possibility theory to take into account the imprecision and the uncertainty of the data. Generally, the number of training samples is not enough to show all the functioning modes of a system. Moreover, predicting the evolution of the functioning mode from normal to abnormal one is useful to avoid the bad consequences. Additionally, in a non-stationary work environment, a classifier must be retrained every time a new sample is classified, to obtain a new knowledge from it. This is an impractical solution since it requires the storage of all available samples and a considerable computation time. The solution is the development of a classifier that is capable to acquire new knowledge from each new classified sample while preserving the current one, this solution is known as incremental learning.The objective of this thesis is to develop FPM to be capable to realize an adaptive and predictive diagnosis in real time and in a non stationary work environment.La supervision automatique des processus industriels permet d'accroître la productivité et de diminuer le coût d'entretien. Le diagnostic est une composante principale d'un module de supervision. Il existe plusieurs approches pour réaliser le diagnostic. Les performances de chaque approche dépendent du problème posé. Nous cherchons une méthode de diagnostic capable de résoudre les problèmes suivants :- dans une base de connaissance incomplète, tous les modes de fonctionnement ne sont pas représentés. En conséquence, un module de diagnostic doit être adaptatif afin d'inclure à sa base de connaissance les nouveaux modes dés qu'ils apparaissent,- lorsque le système évolue vers un mode anormal ou non désiré, il est nécessaire d'anticiper cette évolution plutôt que d'attendre d'arriver à ce mode afin d'éviter ses conséquences surtout s'il est dangereux. Le module de diagnostic doit donc être prédictif,- dans le cas d'un système évolutif, la base de connaissance doit être enrichie grâce à l'information apportée par les nouvelles observations. Cet enrichissement doit être réalisé en temps réel,- les données sont à la fois incertaines et imprécises.L'objectif principal de ma thèse consistait à mettre au point un module de diagnostic en temps réel adaptatif et prédictif pour des systèmes évolutifs, en utilisant les techniques de Reconnaissance des Formes, la théorie des ensembles flous et la théorie des possibilités. Ce module a été appliqué sur plusieurs applications industrielles

Discrete event systems: diagnosis and diagnosability

Discrete Event Systems: Diagnosis and Diagnosability addresses the problem of fault diagnosis of Discrete Event Systems (DES). This book provides the basic techniques and approaches necessary for the design of an efficient fault diagnosis system for a wide range of modern engineering applications. The different techniques and approaches are classified according to several criteria such as: modeling tools (Automata, Petri nets) that is used to construct the model; the information (qualitative based on events occurrences and/or states outputs, quantitative based on signal processing and data analysis) that is needed to analyze and achieve the diagnosis; the decision structure (centralized, decentralized) that is required to achieve the diagnosis. The goal of this classification is to select the efficient method to achieve the fault diagnosis according to the application constraints. This book focuses on the centralized and decentralized event based diagnosis approaches using formal language and automata as modeling tool. The work includes illustrated examples of the presented methods and techniques as well as a discussion on the application of these methods on several real-world problems