On robust estimation of the location parameter

This survey of model-based fault diagnosis focuses on those methods that are applicable to aerospace systems. To highlight the characteristics of aerospace models, generic non-linear dynamical modelling from flight mechanics is recalled and a unifying representation of sensor and actuator faults is presented. An extensive bibliographical review supports a description of the key points of fault detection methods that rely on analytical redundancy. The approaches that best suit the constraints of the field are emphasized and recommendations for future developments in in-flight fault diagnosis are provided.

/pdf/model-based-fault-diagnosis-for-aerospace-systems-a-survey-jpkifgdebi.pdf

Model-based fault diagnosis for aerospace systems: a survey:

Activated Sludge Model No.2를 이용한 하수의 생물학적 질소·인 제거 공정의 처리성 평가

This paper surveys and discusses the application of data-derived soft-sensing techniques in biological wastewater treatment plants. Emphasis is given to an extensive overview of the current status and to the specific challenges and potential that allow for an effective application of these soft-sensors in full-scale scenarios. The soft-sensors presented in the case studies have been found to be effective and inexpensive technologies for extracting and modelling relevant process information directly from the process and laboratory data routinely acquired in biological wastewater treatment facilities. The extracted information is in the form of timely analysis of hard-to-measure primary process variables and process diagnostics that characterize the operation of the plants and their instrumentation. The information is invaluable for an effective utilization of advanced control and optimization strategies. We review data-derived soft-sensors proposed for biological wastewater treatment.Increased amount of measured process data has made data-driven modelling attractive.A general guideline for the data-derived soft-sensor development is presented.Artificial intelligence and multivariate statistical methods are popular in soft-sensor design.Popularity of the on-line prediction applications has increased during recent years.

Review: Data-derived soft-sensors for biological wastewater treatment plants: An overview

Fault detection is often utilized for proper operation of chemical processes. In this paper, a nonlinear statistical fault detection using kernel principal component analysis (KPCA)-based generalized likelihood ratio test (GLRT) is proposed. The objective of this work is to extend our previous work (Harrou et al. (2013) to achieve further improvements and widen the applicability of the developed method in practice by using the KPCA method. The KPCA presented here is derived from the nonlinear case of principal component analysis (PCA) algorithm and it is investigated here as modeling algorithm in the task of fault detection. The fault detection problem is addressed so that the data are first modeled using the KPCA algorithm and then the faults are detected using GLRT. The detection stage is related to the evaluation of detection indices, which are signals that reveal the fault presence. Those indices are obtained from the analysis of the difference between the process measurements and their estimations using the KPCA technique. The fault detection performance is illustrated through two simulated examples, one using synthetic data and the other using simulated continuously stirred tank reactor (CSTR) data. The results demonstrate the effectiveness of the KPCA-based GLRT method over the conventional KPCA method through its two charts T2 and Q for detection of single as well as multiple sensor faults.

Kernel PCA-based GLRT for nonlinear fault detection of chemical processes

Principal component analysis (PCA) is a powerful fault detection and isolation method. However, the classical PCA, which is based on the estimation of the sample mean and covariance matrix of the data, is very sensitive to outliers in the training data set. Usually robust principal component analysis is applied to remove the effect of outliers on the PCA model. In this paper, a fast two-step algorithm is proposed. First, the objective was to find an accurate estimate of the covariance matrix of the data so that a PCA model might be developed that could then be used for fault detection and isolation. A very simple estimate derived from a one-step weighted variance-covariance estimate is used (Ruiz-Gazen, 1996). This is a "local" matrix of variance which tends to emphasize the contribution of close observations in comparison with distant observations (outliers). Second, structured residuals are used for multiple fault detection and isolation. These structured residuals are based on the reconstruction principle, and the existence condition of such residuals is used to determine the detectable faults and the isolable faults. The proposed scheme avoids the combinatorial explosion of faulty scenarios related to multiple faults to be considered. Then, this procedure for outliers detection and isolation is successfully applied to an example with multiple faults.

/pdf/fault-detection-and-isolation-with-robust-principal-1r600q4ot4.pdf

Fault Detection and Isolation with Robust Principal Component Analysis

WWTP diagnosis based on robust principal component analysis

Sensors are essential components of modern control systems. Any faults in sensors will affect the overall performance of a system because their effects can easily propagate to manipulative variables through feedback control loops and also disturb other process variables. The task for sensor validation is to detect and isolate faulty sensors and estimate fault magnitudes afterwards to provide fault-free values. Model-based methods constitute an important approach to sensor fault detection and isolation (FDI). A model-based approach consists in generating residuals as the difference between the measurements and the estimates provided by the relationships existing between the various variables of the process. The analysis of these residuals may lead to detect and isolate the faulty sensors. Almost all conventional model-based methods presume the knowledge of an accurate model of the system, e.g. transfer function or system matrices in the state space representation. Principal component Analysis (PCA) is a data-driven method which is particularly well adapted to reveal linear relationships among the plant variables without formulating them explicitly and has also been employed for system identification. PCA has some other nice features. It can handle high dimensional and correlated process variables, provides a natural solution to the errors-in-variables problem and includes disturbance decoupling (Li & Qin, 2001). Moreover in the FDI field, Gertler & McAvoy (1997) have shown a close link between PCA and parity space method. Principal component analysis (PCA) has then been applied successfully in the monitoring of complex systems (Chiang & Colegrove, 2007; Harkat et al., 2006; Kano & Nakagawa, 2008). 17

/pdf/sensor-fault-detection-and-isolation-by-robust-principal-4y82lkswri.pdf

Sensor Fault Detection and Isolation by Robust Principal Component Analysis

This paper presents a data-driven method based on non-linear principal component analysis to detect and isolate multiple sensor faults. The RBF-NLPCA model is obtained by combining a principal curve algorithm and two three-layer radial basis function (RBF) networks. The reconstruction approach for multiple sensors is proposed in the non-linear case and successfully applied for multiple sensor fault detection and isolation of an air quality monitoring network. The proposed approach reduces considerably the number of reconstruction combinations and allows to determine replacement values for the faulty sensors.

Multiple sensor fault detection and isolation of an air quality monitoring network using RBF-NLPCA model

L'Analyse en Composantes Principales (ACP) est largement utilisee pour le diagnostic de fonctionnement de systeme Cependant, l'un des inconvenients majeurs de l'ACP resulte de l'utilisation de techniques d'estimation par moindres carres, techniques qui echouent souvent a s'affranchir des biais de mesure accidentels Ce papier presente une procedure de detection et de localisation de defauts de mesure La methode proposee ne necessite pas d'etude preliminaire relative a la detection et au rejet de valeurs aberrantes dans les donnees utilisees pour la conception du modele ACP Le modele ACP obtenu etant robuste, c'est-a-dire non contamine par les valeurs aberrantes, son utilisation pour le diagnostic est alors efficace

https://hal.archives-ouvertes.fr/hal-00170253/document

Yvon Tharrault

Papers

Fault Detection and Isolation with Robust Principal Component Analysis

WWTP diagnosis based on robust principal component analysis

Sensor Fault Detection and Isolation by Robust Principal Component Analysis

Multiple sensor fault detection and isolation of an air quality monitoring network using RBF-NLPCA model

Détection et isolation de défauts par analyse en composantes principales robuste