Joint diagnosis of process and sensor faults using principal component analysis

Dunia, Ricardo; Qin, S. Joe

doi:10.1016/s0967-0661(98)00027-6

Cited by 215 publications

(119 citation statements)

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“…The research conducted on the fault diagnosis problem that deals simultaneously with process and sensor faults is limited (Dunia & Joe Qin, 1998;Salahshoor, Mosallaei, & Bayat, 2008;Q. Zhang & Zhang, 2012;X.…”

Section: Introductionmentioning

confidence: 99%

Distributed fault diagnosis for process and sensor faults in a class of interconnected input–output nonlinear discrete-time systems

Keliris

Polycarpou

Parisini

2015

International Journal of Control

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This paper presents a distributed fault diagnosis scheme able to deal with process and sensor faults in an integrated way for a class of interconnected input-output nonlinear uncertain discrete-time systems. A robust distributed fault detection scheme is designed, where each interconnected subsystem is monitored by its respective fault detection agent, and according to the decisions of these agents, further information regarding the type of the fault can be deduced. As it is shown, a process fault occurring in one subsystem can only be detected by its corresponding detection agent whereas a sensor fault in a subsystem can be detected by either its corresponding detection agent or the detection agent of another subsystem that is affected by the subsystem where the sensor fault occurred. This discriminating factor is exploited for the derivation of a high-level isolation scheme. Moreover, process and sensor fault detectability conditions characterizing quantitatively the class of detectable faults are derived. Finally, a simulation example is used to illustrate the effectiveness of the proposed distributed fault detection scheme.

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Section: Introductionmentioning

confidence: 99%

Distributed fault diagnosis for process and sensor faults in a class of interconnected input–output nonlinear discrete-time systems

Keliris

Polycarpou

Parisini

2015

International Journal of Control

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“…A simple approach is to choose the number of PCs for the variance to achieve a predetermined percentage, such as 85%, Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering) ISSN 1673-565X (Print); ISSN 1862-1775 (Online) www.zju.edu.cn/jzus; www.springerlink.com E-mail: jzus@zju.edu.cn termed as cumulative percent variance (CPV) (Jackson, 1991). Other methods for determination, including cross validation, average eigenvalue approach, variance of reconstruction error (VRE) criterion, and fault signal-to-noise ratio (fault SNR), have been proposed to determine the number of the retained PCs (Wold, 1978;Dunia and Qin, 1998;Valle et al, 1999;Tamura and Tsujita, 2007). The cross validation method uses part of the training samples for the model construction, while the rest are compared with the prediction by the model.…”

Section: Introductionmentioning

confidence: 99%

Statistical process monitoring based on improved principal component analysis and its application to chemical processes

Tong

Yan

2013

J. Zhejiang Univ. Sci. A

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Abstract:In this paper, a novel criterion is proposed to determine the retained principal components (PCs) that capture the dominant variability of online monitored data. The variations of PCs were calculated according to their mean and covariance changes between the modeling sample and the online monitored data. The retained PCs containing dominant variations were selected and defined as correlative PCs (CPCs). The new Hotelling's T 2 statistic based on CPCs was then employed to monitor the process. Case studies on the simulated continuous stirred tank reactor and the well-known Tennessee Eastman process demonstrated the feasibility and effectiveness of the CPCs-based fault detection methods.

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“…11). Related ideas using principal component analysis are found in the chemical diagnosis literature as (Chiang et al, 2001;Dunia and Qin, 1998) This work is a continuation of (Gustafsson, 2001), where an additive fault was included in an augmented state vector, and observability of the fault was used as the tool to assess diagnosability. In this paper, an explicit expression for P i,j = P (diagnosis j| fault i) is given for any parity space, and the parity space is optimally designed to minimize these probabilities in order to improve sensitivity issues in diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Fault Diagnosability in Parity Spaces

Gustafsson¹

2002

IFAC Proceedings Volumes

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We here analyze the parity space approach to fault detection and isolation in a stochastic setting. Using a state space model with both deterministic and stochastic unmeasurable inputs we show a formal relationship between the Kalman filter and the parity space. Based on a statistical fault detection and diagnosis algorithm, the probability for incorrect diagnosis is computed explicitly, given that only a single fault with known time profile has occurred. An example illustrates how the matrix of diagnosis probabilities can be used as a design tool for performance optimization with respect to, for instance, design variables and sensor placement and quality.

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Joint diagnosis of process and sensor faults using principal component analysis

Cited by 215 publications

References 0 publications

Distributed fault diagnosis for process and sensor faults in a class of interconnected input–output nonlinear discrete-time systems

Distributed fault diagnosis for process and sensor faults in a class of interconnected input–output nonlinear discrete-time systems

Statistical process monitoring based on improved principal component analysis and its application to chemical processes

Stochastic Fault Diagnosability in Parity Spaces

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