Design of a novel knowledge-based fault detection and isolation scheme

Authors:
Qing Zhao;Zhihan Xu
Affiliations:
Dept. of Electr. & Comput. Eng., Univ. of Alberta, Edmonton, Alta., Canada;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2004

Citing 0
Cited 2

Optimal fault-detection filtering for non-Gaussian systems via output PDFs

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A new fault tolerant control approach for the three-tank system using data mining

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Abstract

In this paper, a real-time fault detection and isolation (FDI) scheme for dynamical systems is developed, by integrating the signal processing technique with neural network design. Wavelet analysis is applied to capture the fault-induced transients of the measured signals in real-time, and the decomposed signals are pre-processed to extract details about a fault. A Regional Self-Organizing feature Map (R-SOM) neural network is synthesized to classify the fault types. The R-SOM neural network adopts two regions adjustment in the learning algorithm, thus it has high precision in clustering and matching, especially when the noise, disturbance and other uncertainties exist in the systems. As a result, the proposed FDI scheme is robust and accurate. The design is implemented on a stirred tank system and satisfactory online testing results are obtained.