Résumé: In this paper a new algorithm for adaptive kernel principal component analysis (AKPCA) is proposed for dynamic process monitoring. The proposed AKPCA algorithm combine two existing algorithms, the recursive weighted PCA (RWPCA) and the moving window kernel PCA algorithms. For fault detection and isolation, a set of structured residuals is generated by using a partial AKPCA models. Each partial AKPCA model is performed on subsets of variables. The structured residuals are utilized in composing an isolation scheme, according to a properly designed incidence matrix. The results for applying this algorithm on the nonlinear time varying processes of the Tennessee Eastman shows its feasibility and advantageous performances.

Résumé: This paper proposes a new online principal component analysis (PCA) index-based parameter estimation approach to detect a sensor fault. The proposed index is based on PCA technique and Mahalanobis distance and it is entitled principal component Mahalanobis distance (PCMD). The principle of the proposed PCMD is to detect a disagreement between the reference PCA model parameter that represent a normal system function and the PCA model parameter that estimated online to represent current system behavior. Indeed, the PCMD index evaluate the Mahalanobis distance between the principal components (PCs) of the reference PCA model and the new PCs that represent the current function of the system. These PCs are determined online using a Moving Window PCA technique (MWPCA). To evaluate performances of the proposed index, PCMD is applied on a numerical example and a chemical reactor (CSTR), and the results are satisfactory compared to other index such as SPCA and Spca

Résumé: Induction motors (IMs) are currently among the most used in variable speed drives due to their high reliability and their low costs. In spite of these qualities, they are more or less penalized by some drawbacks, such as low efficiency, low start-up torque and the likelihood of some rotor failures. Within the last drawback, the paper deals with the diagnosis and detection of the IM rotor dynamic eccentricity fault. Firstly, a dedicated IM model is derived taking into account both healthy and faulty operation cases. Then, simulation works are carried out focusing the IM no-load start-up followed by the application of a load torque at steady-state, considering both healthy and faulty operation cases. A special attention is paid to the analysis of the stator current whose harmonic spectrum highlights some specific frequencies around the fundamental one. These represent signatures for the detection and the localization of the IM rotor dynamic eccentricity fault.

Résumé: Induction motors have been extensively integrated in most if not all industrial fields, covering a wide range of power, within both grid-connected and variable speed drives. Of particular interest is the squirrel cage induction motor which is popular thanks to its low cost and the robustness of its rotor whose circuits do not require any slip-ring systems. However, the squirrel cage induction motor suffers from relatively frequent faults mainly due to given rotor failures. In this paper, a dedicated model of the squirrel cage induction motor, taking into account, as accurately as possible, the rotor equivalent circuit, is firstly derived. Then a case study of broken bar faults is treated, considering both spectral and d-q phasor analysis of the stator phase-currents

Résumé: The induction motor is one of the most used electric machines in variable speed system in the different field of industry and takes a particular interest for applications requiring high power and variable speed for its robust and simplicity. The early detection for motor deterioration can increase plant availability and safety in an economical way. Many publications have investigated the detection and diagnosis broken rotor bars in electrical machines supplied directly on line. However, much fewer research results have been published when the induction motor is fed by pulse width modulation (PWM) voltage source inverter which is the most common drive in the industry. This paper presents a technique method based on spectral analysis of stator currents to detect broken rotor bars fault in the rotor when it is fed from PWM-VSI. The obtained results show clearly the possibility of extracting signatures to detect and locate fault.

Résumé: In this paper a sensor fault detection and isolation procedure based on principal component analysis is proposed to monitor an air quality monitoring network. The PCA model of the network is optimal with respect to a reconstruction error criterion. The sensor fault detection is carried out in various residual subspaces using a new detection index. For our application, this index improves the performance compared to classical detection index SPE. The reconstruction approach allows, on one hand, to isolate the faulty sensors and, on the other hand, to estimate the fault amplitudes.

Résumé: Nous proposons une méthode basée sur l'analyse en composantes principales pour la détection et la localisation de défauts de capteurs d'un réseau de surveillance de la qualité de l'air. Le modèle ACP du réseau de mesures est optimal au sens d'un critère basé sur l'erreur de reconstruction des différentes variables. La détection des défauts de capteurs est réalisée dans différents sous-espaces résiduels à  l'aide d'un nouvel indicateur de détection. Enfin, la reconstruction des variables permet, d'une part, en la combinant avec l'indicateur de détection, de localiser les capteurs défaillants et, d'autre part, d'estimer l'amplitude des défauts

Résumé: Data-Driven and Model-Based Methods for Fault Detection and Diagnosis covers techniques that improve the quality of fault detection and enhance monitoring through chemical and environmental processes. The book provides both the theoretical framework and technical solutions. It starts with a review of relevant literature, proceeds with a detailed description of developed methodologies, and then discusses the results of developed methodologies, and ends with major conclusions reached from the analysis of simulation and experimental studies. The book is an indispensable resource for researchers in academia and industry and practitioners working in chemical and environmental engineering to do their work safely.

Résumé: The main objective of Data-Driven and Model-Based Methods for Fault Detection and Diagnosis is to develop techniques that improve the quality of fault detection and then utilize these developed techniques to enhance monitoring various chemical and environmental processes. The book provides both the theoretical framework and technical solutions. It starts with reviewing relevant literature, proceeds with a detailed description of developed methodologies, followed by a discussion of the results of developed methodologies, and ends with major conclusions reached from the analysis of simulation and experimental studies. The book is an indispensable resource for researchers in academia and industry and practitioners working in chemical and environmental engineering to do their work safely.

Résumé: This paper proposes a novel index to detect a sensor fault based on principal component analysis (PCA). The main idea behind this index is to evaluate similarity between Principal Components that represent a normal behavior of a system and a new that represent current function. This index is evaluated on a Chemical Reactor (CSTR) and the results are satisfactory

Résumé: Principal component analysis (PCA) is a commonly used approach to process monitoring. However, it has been developed for singleton variables. Whereas, in many real life cases, this leads to a severe loss of information, this can be overcome by introducing the interval notion. The present paper deals with the study of fault detection and isolations (FDI) of uncertain process using interval PCA. Interval data are generated according to various models, and the FDI procedure is lead using the reconstruction principle technique, in its new interval form, for three interval PCA methods: Vertices PCA, Centers PCA, and Midpoints/Radius PCA. A comparison is presented where it is reported in which conditions each method performs best for FDI purpose.

Résumé: This paper presents a new adaptive kernel principal component analysis algorithm (AKPCA) for nonlinear time-varying process monitoring. The basic idea is to use a neuronal principal component analysis based on the kernel version of the generalized Hebbian algorithm. The proposed algorithm follows a new methodology to update the KPCA model. At each time instant, when a new data is available, the KPCA model is updated accordingly without having to re-explore all previous data. By using the proposed algorithm, the performance of process monitoring is improved in two aspects; the speed computation and adaptation of the KPCA model, and the storage memory complexity. To identify faults in a dynamic process, the reconstruction based contribution approach is used and adapted in real time. The results for applying this algorithm on the Tennessee Eastman process shows its feasibility and advantageous performances.