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Process fault isolation based on transfer entropy algorithm
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DOI：10.1016/j.isatra.2013.11.007.png)
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Abstract
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Complexity of industrial plants and their stringent environmental and safety regulations have necessitated early detection and isolation of process faults. All the existing fault isolation methods can be categorized into two general groups: model-based and data-based. Transfer entropy is a data-based method for measuring propagation direction of disturbance and finding its root cause. In this paper, a new transfer entropy-based method is proposed to isolate different process faults. The novelty of this paper lies in using the transfer entropy idea to generate distinct patterns of information flow among process variables, recognize their correlations in the context of the transferred information in any abnormal condition, and finally isolate different process faults. The experimental results clearly demonstrate the superiority of the proposed method to the conventional methods. (C) 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Keywords:
Fault detection and isolation
Transfer entropy
Probability density function
Classifier
Wavelet
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