B-Spline Network Integrated Qualitative and Quantitative Fault Detection

Benkhedda, H.; Patton, Ron J.

doi:10.1016/s1474-6670(17)58722-4

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Many books are available that provide an introduction to neural networks [24,28,61,156,309,358]. Numerous papers are available which apply ANNs to fault detection and diagnosis; many of these techniques were derived from the pattern recognition perspective [17,26,50,49,106,118,119,124,210,250,297,313,333,334,361,365].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Fault Detection and Diagnosis in Industrial Systems

Chiang

Russell

Braatz

2001

Advanced Textbooks in Control and Signal Processing

1,133

811

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An alternative group of methods has emerged which do not require the use of an explicit model. This is the key basic construct for the data-driven paradigm. Model-free and non-parametric methods for fault detection, process optimisation and control design are currently at a particularly exciting stage of development.This new advanced textbook by Chiang, Russell and Braatz primarily tackles the data-driven routes to Fault Detection and Diagnosis. It is an outgrowth of a prior Advances in Industrial Control monograph; Russell, Chiang and Braatz. Data-driven Techniques for Fault Detection and Diagnosis in ChemicalProcesses, 2000, ISBN 1-85233-258-1. The new textbook expands the material of the monograph and gives a fuller presentation of some of the alternative modelbased methods, the analytical methods, and of the knowledge-based techniques. vi Series Editors' ForewordThis allows the reader to compare and contrast the different approaches to the problem of fault detection and diagnosis. Thus the text is suitable for advanced courses for process, chemical and control engineers.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Fault Detection and Diagnosis in Industrial Systems

Chiang

Russell

Braatz

2001

Advanced Textbooks in Control and Signal Processing

1,133

811

View full text Add to dashboard Cite

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“…); moreover, the derivative of basis spline function can be readily obtained. Further, the training of the BSNN is more quickly than other networks(e.g., multilayer feedforword network) and has been applied for guidance[ 81, fault detection and isolation [9], Kalman filter initialisation[ 101. Here we discuss the recurrent networks based on BSNN.…”

Section: Introductionmentioning

confidence: 99%

B-spline recurrent neural network and its application to modelling of non-linear dynamic systems

Chan¹,

Cheung²,

Jin³

et al. 1998

Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207)

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A new recurrent neural network based on Bspline function approximation is presented. The network can be easily trained and its training converges more quickly than that for other recurrent neural networks. Moreover, an adaptive weight updating algorithm for the recurrent network is proposed. It can speed up the training process of the network greatly and its learning speed is more quickly than existing algorithms, e.g., back-propagation algorithm. Examples are presented comparing the adaptive weight updating algorithm and the constant learning rate method, and illustrating its application to modelling of nonlinear dynamic system.

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“…a fault detection and isolation problem has been the use of artificial neural networks (ANNs). Several ANN types have been considered, using feedforward networks, radial basis functions, B-spline networks, recurrent net-2 TAKAGI-SUGENO FUZZY MODEL AND works and neuro-fuzzy systems [7][8][9][10][11]. The main prob-STABILITY ANALYSIS lem with these approaches is the difficulty in analysing, in a rigorous mathematical way, their robustness and 2.1 Takagi-Sugeno fuzzy model sensitivity.…”

mentioning

confidence: 99%

“…Rule i (i=1, 2, ..., N) techniques. It is only recently that some researchers have attempted to combine these techniques together [11,[13][14][15]. Instead of employing either approach alone…”

mentioning

confidence: 99%

Non-linear dynamic systems fault detection and isolation using fuzzy observers

Chen

Lopez-Toribio

Patton

1999

Proceedings of the Institution of Mechanical Engineers, Part I:

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This paper presents a novel fault detection and isolation scheme for non-linear dynamic systems. This scheme utilizes a fuzzy observer to generate the diagnostic residual signal for fault detection and isolation. This fuzzy observer, based upon the idea of Takagi-Sugeno fuzzy models, comprises a number of locally linear observers and the final state estimate is a fuzzy fusion of all local observer outputs. To ensure good estimation performance, the eigenvalues of the fuzzy observer are assigned in a pre-defined region in the complex plane. The stability as well as eigenvalue constraint conditions for the fuzzy observer design are presented and solved in the linear matrix inequality framework in this paper. Finally, the paper demonstrates the application of fuzzy observers in detecting and isolating intermittent faults in the induction motor of a railway traction system using a real-time test-rig implementation.

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B-Spline Network Integrated Qualitative and Quantitative Fault Detection

Cited by 6 publications

References 10 publications

Fault Detection and Diagnosis in Industrial Systems

Fault Detection and Diagnosis in Industrial Systems

B-spline recurrent neural network and its application to modelling of non-linear dynamic systems

Non-linear dynamic systems fault detection and isolation using fuzzy observers

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