Observer-Based Optimal Fault Detection and Diagnosis Using Conditional Probability Distributions

Guo, Lei; Zhang, Yumin; Wang, Hong; Fang, Jiancheng

doi:10.1109/tsp.2006.879314

Cited by 54 publications

(28 citation statements)

References 19 publications

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“…There is no need to measure the system output pdf. This constitutes a major advantage compared with [37][38][39], which require the output pdf to be measurable.…”

Section: Discussionmentioning

confidence: 99%

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Function Based Fault Detection for Uncertain Multivariate Nonlinear Non-Gaussian Stochastic Systems Using Entropy Optimization Principle

Yin

Zhou

2012

Entropy

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In this paper, the fault detection in uncertain multivariate nonlinear non-Gaussian stochastic systems is further investigated. Entropy is introduced to characterize the stochastic behavior of the detection errors, and the entropy optimization principle is established for the fault detection problem. The principle is to maximize the entropies of the stochastic detection errors in the presence of faults and to minimize the entropies of the detection errors in the presence of disturbances. In order to calculate the entropies, the formulations of the joint probability density functions (JPDFs) of the stochastic errors are presented in terms of the known JPDFs of both the disturbances and the faults. By using the novel performance indexes and the formulations for the entropies of the detection errors, new fault detection design methods are provided for the considered multivariate nonlinear non-Gaussian plants. Finally, a simulation example is given to illustrate the efficiency of the proposed fault detection algorithm.

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“…There is no need to measure the system output pdf. This constitutes a major advantage compared with [37][38][39], which require the output pdf to be measurable.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, as a branch of stochastic distribution control (SDC), fault detection using entropy optimization principle still remains in the theoretic research stage [8,37,40]. Its real world application requires more research efforts.…”

Section: Discussionmentioning

confidence: 99%

Function Based Fault Detection for Uncertain Multivariate Nonlinear Non-Gaussian Stochastic Systems Using Entropy Optimization Principle

Yin

Zhou

2012

Entropy

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“…Once it has been confirmed that the non-Gaussian PDF is caused by the process fault, further fault diagnosis analysis is performed to locate the fault and estimate its size. In this context, observer based fault diagnosis can be used, where PDF residuals [38][39]48] are constructed for the sequential PDF models. Adaptive tuning rule-based fault diagnosis methods can be developed so as to guarantee the performance of the fault diagnosis.…”

Section: Pdf Based Fdd and Ftc Using Multi-scale Plant-wide Modelsmentioning

confidence: 99%

Data Driven Fault Diagnosis and Fault Tolerant Control: Some Advances and Possible New Directions

Wang¹,

Chai²,

Ding³

et al. 2009

Acta Automatica Sinica

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This paper presents a selected survey covering the advances of fault diagnosis and fault tolerant control using data driven techniques. A brief summary of the general developments in fault detection and diagnosis for industrial processes is given, which is then followed by discussions on the widely used data driven and knowledge-based techniques. A successful application example is also given, which deals with faults caused by the misplacement of control loop set points and several areas of potential future directions are included in the paper. Key words Fault detection, fault diagnosis, fault tolerant control, data driven techniques Fault detection and diagnosis (FDD) and fault tolerant control (FTC) have been the subject of considerable interest in the control research community [1−39] . This is in response to the ever increasing requirements on the reliable operation of control systems, which are, in most cases, subject to a number of faults either in the internal closed loops or from environmental factors. Once system faults have occurred, they can cause unrecoverable losses and result in unacceptable environmental pollution, etc. Occasionally, the occurrence of a minor fault has resulted in disastrous effects. For example, it has been observed that faults have caused a 3 % ∼ 8 % reduction of the oil production in the United States, leading to $ 20 billion losses in the country s economy per year. Also, in 1997, the faults in a chemical plant in Beijing caused heavy direct losses. Therefore, effective FDD is of vital importance to the safe operation of industrial plants. Indeed, FDD and FTC have now become an integral part of industrial process control.In general, system faults can be grouped into several categories, namely, actuator faults, sensor faults, system faults and also abnormal operating faults caused by either the misplacement of control loop set points or unexpected variations in the raw materials to be processed. The purpose of FDD is to use available signals to detect, identify, and isolate possible sensor faults, actuator faults, and system faults. Conversely, FTC calculates the required actions (either controller modification or reconfiguration) so that the system can still continue to operate safely even under faulty conditions [2][3]40] . In terms of condition monitoring or FDD, the existing methods can also be grouped into the following two categories: 1) Model based FDD; 2) Data driven FDD including knowledge based FDD. In the early days (1980 s onwards), model based FDD constituted the main stream of research, and a number of techniques were developed. Depending on whether the system model can be represented as either a state space model or an input-output model, FDD can be classified into two groups: observer based FDD [1] and system identification based FDD [4] . Also, to combine the best features of these two approaches, there is another group of FDD methods called adaptive observer based fault diagnosis, which uses parameter tuning principles from model reference adap- tive ...

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“…So there is a need to further develop the FDD methods that can be applied to the stochastic systems subject to non-Gaussian distribution. Motivated by these factors, studies on stochastic distribution systems and stochastic distribution control have been investigated in [1,3,4,6,7,10,19,20,[22][23][24][25][31][32][33]. Differently from conventional FDD problems, the measurement information for the FDD is the output PDFs rather than the mean or variance of the output, and the stochastic variables involved in are not confined to the Gaussian ones.…”

Section: Introductionmentioning

confidence: 99%

Fault Tolerant Control for Non-Gaussian Stochastic Distribution Systems

2012

Circuits Syst Signal Process

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A new fault tolerant control (FTC) problem via the output probability density functions (PDFs) for non-Gaussian stochastic distribution control systems (SDC) is investigated. The PDFs can be approximated by the radial basis functions (RBFs) of neural networks. Differently from the conventional FTC problems, the measured information is in the form of probability distributions of the system output rather than the actual output values. The control objective is to use the output PDFs to design control algorithm that can compensate the faults and attenuate the disturbances. As a result, the concerned FTC problem subject to dynamic relation between the input and output PDFs can be transformed into a nonlinear FTC problem subject to dynamic relation between the control input and the weights of the RBFs neural networks. Feasible criteria to compensate the faults and attenuate the disturbances are provided in terms of linear matrix inequality (LMI) techniques. In order to improve FTC performances, H ∞ optimization techniques are applied to the FTC design problem to assure that the faults can be compensated and the disturbances can be attenuated. At last, an illustrated example is given to demonstrate the efficiency of the proposed algorithm, and the satisfactory results have been obtained.

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Observer-Based Optimal Fault Detection and Diagnosis Using Conditional Probability Distributions

Cited by 54 publications

References 19 publications

Function Based Fault Detection for Uncertain Multivariate Nonlinear Non-Gaussian Stochastic Systems Using Entropy Optimization Principle

Function Based Fault Detection for Uncertain Multivariate Nonlinear Non-Gaussian Stochastic Systems Using Entropy Optimization Principle

Data Driven Fault Diagnosis and Fault Tolerant Control: Some Advances and Possible New Directions

Fault Tolerant Control for Non-Gaussian Stochastic Distribution Systems

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