Diagnostic précoce des défauts en ligne à base d’un classifieur dynamique hybride : application aux éoliennes

L'objectif principal de cette thèse est de développer un schéma générique et adaptatif basée sur les approches d'apprentissage automatique, intégrant des mécanismes de détection et d'isolation des défauts avec une force d’apparition progressive. Le but de ce schéma est de réaliser le diagnostic en ligne des défauts simple et multiple de type dérive dans les systèmes éoliens, et plus particulièrement dans le système du calage des pales et le convertisseur de puissance. Le schéma proposé est basé sur la décomposition du système éolien en plusieurs composantes. Ensuite, un classifieur est conçu et utilisé pour réaliser le diagnostic de défauts dans chaque composant. Le but de cette décomposition en composants est de faciliter l'isolation des défauts et d'augmenter la robustesse du schéma globale de diagnostic dans le sens que lorsque le classifier lié à un composant est défaillant, les classifieurs liées aux autres composants continuent à réaliser le diagnostic des défauts dans leurs composants. Ce schéma a aussi l'avantage de prendre en compte la dynamique hybride de l’éolienne.This thesis addresses the problem of automatic detection and isolation of drift-like faults in wind turbines (WTs). The main aim of this thesis is to develop a generic on-line adaptive machine learning and data mining scheme that integrates drift detection and isolation mechanism in order to achieve the simple and multiple drift-like fault diagnosis in WTs, in particular pitch system and power converter. The proposed scheme is based on the decomposition of the wind turbine into several components. Then, a classifier is designed and used to achieve the diagnosis of faults impacting each component. The goal of this decomposition into components is to facilitate the isolation of faults and to increase the robustness of the scheme in the sense that when the classifier related to one component is failed, the classifiers for the other components continue to achieve the diagnosis for faults in their corresponding components. This scheme has also the advantage to take into account the WT hybrid dynamics. Indeed, some WT components (as pitch system and power converter) manifest both discrete and continuous dynamic behaviors. In each discrete mode, or a configuration, different continuous dynamics are defined

Fault-Detection-Based Machine Learning Approach to Multicellular Converters Used in Photovoltaic Systems

Today, solar energy systems based on photovoltaic (PV) panels associated with power converters are increasingly used to supply isolated sites. This structure has attracted several studies as a cost-effective, freely available, efficient source of clean and low-cost energy. However, the faults in power converters can affect the stability of the control system by supplying the isolated site with unwanted current and voltage. Therefore, this paper presents a comparative study using a fault-detection-based k-nearest neighbor (KNN) approach, between sliding mode control and exact linearization control applied to an isolated PV-system-based multicellular power converter, in order to assess the robustness and the performance of the two control strategies against the flying capacitor faults. The results obtained for both control methods in different fault cases are analyzed in terms of time series and feature spaces. These results, obtained with MATLAB software, prove the superiority of sliding mode control over exact linearization control in terms of response time, precision, and oscillations of flying capacitor voltages, as well as better separation (classification) between different fault cases in feature space

Health management using fault detection and fault tolerant control of multicellular converter applied in more electric aircraft system

The increased cost of fuel and maintenance in aircraft system lead to the concept of more electric aircraft, moreover this concept increase the use of power electronic converters in aircraft power system. Since in this application, the reliability is a crucial feature. Therefore, the use of more efficient, reliable and robust power converter with health management capability will be a big challenge. Multicellular topology of power converters has the required performance in terms of efficiency and robustness. However, the increased complexity of control and more power components (power switches and capacitors) goes along with an increase in possibility of failure in multicellular topology. Therefore, the main contribution of this paper is the use of multicellular topology advantageous with fault diagnosis and fault tolerant control in order to increase the robustness reliability. The health management using a fault detection with Fuzzy Pattern Matching (FPM) algorithm when a failure in power switches or flying capacitors of multicellular converter and a Fault Tolerant Control (FTC) with sliding mode of second parallel three cells multicellular converters. Simulation results with Matlab show the increased efficiency and the continuity of work during failure mode in aircraft power system

Self adaptive learning scheme for early diagnosis of simple and multiple switch faults in multicellular power converters

International audienceThis paper proposes a scheme based on the use of unsupervised machine learning approach and a drift detection mechanism in order to perform an early fault diagnosis of simple and multiple stuck-opened/stuck-closed switches in multicellular converters. Only the data samples representing the normal operation conditions are used in order to be adapted to the case where no data is available about faulty behaviors. A health indicator measuring the dissimilarity between normal and current operation conditions is built in order to detect a drift (degradations) in early stage. When a degradation (fault) is detected, the isolation is achieved by taking into account the discrete dynamics of switches. The features related to the latter are extracted in order to build a feature space allowing to separate the faulty behavior (zone or class) of the different switches. The proposed scheme is evaluated using real data samples representing different normal/simple/multiple switch fault scenarios issued from a test rig

Fault Diagnosis Based Machine Learning and Fault Tolerant Control of Multicellular Converter Used in Photovoltaic Water Pumping System

Currently, providing water in developing countries, especially in dry and hot rural areas, is a significant challenge. However, creating new electric grids is often expensive. Therefore, the use of low-cost photovoltaic (PV) panels in water pumping systems, without chemical energy storage, based on high-performance and more efficient power converters with increased time life and lower maintenance interventions is needed. In this study, a photovoltaic water pumping system with two power converters, the first is used to extract the maximum power using the maximum power point tracking (MPPT) algorithm, and the second is a three-cell multicellular power converter used to control the DC motor with a submerged pump. Meanwhile, the serial connection and redundant topology of multicellular converters render the system more vulnerable to failure. fault diagnosis-based machine learning approach and fault tolerant control (FTC) are proposed for multicellular power converters. Simulation results with MATLAB show the effectiveness and practicability of the proposed structure and control to isolate the faulty capacitor, increase the sustainability of the system, assure the supply of water under faulty conditions, minimize the mechanical vibrations in electric DC motors, and avoid PV system shutdown

Intelligent Fault Analysis Decision Flow in Semiconductor Industry 4.0 Using Natural Language Processing with Deep Clustering

International audienceMicroelectronics production failure analysis is a time-consuming and complicated task involving successive steps of analysis of complex process chains. The analysis is triggered to find the root cause of a failure and its findings, recorded in a reporting system using natural language. Fault analysis, physical analysis, sample preparation and package construction analysis are arguably the most used analysis activity for determining the root-cause of a failure. Intelligent automation of this analysis decision process using artificial intelligence is the objective of the FA 4.0 consortium; creating a reliable and efficient semiconductor industry. This research presents natural language processing (NLP) techniques to find a coherent representation of the expert decisions during fault analysis. The adopted methodology is a Deep learning algorithm based on β-variational autoencoder (β-VAE) for latent space disentanglement and Gaussian Mixture Model for clustering of the latent space for class identification