Effective fault detection and isolation using bond graph-based domain decomposition
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- 作者：Xi Zhang ; Karlene A. Hoo
- 关键词：Wastewater treatment plant ; Principal component analysis ; Wavelet transform ; Bayesian network
- 刊名：Computers and Chemical Engineering
- 出版年：2011
- 出版时间：10 January 2011
- 年：2011
- 卷：35
- 期：1
- 页码：132-148
- 全文大小：2689 K
文摘
The problem of fault detection and isolation in complex plants can be effectively addressed by a hierarchical strategy involving successive narrowing of the search space of potential faults. A bond graph network is one means of achieving a hierarchical strategy based on the physical domains present in the plant. First, the multivariate statistical method of principal component analysis is used to reduce the data dimension. Second, a discrete wavelet transform is applied to abstract the dynamics at different scales. Thirdly, the Mahalanobis distance is applied to calculate the confidence level. Following a conclusion of the existence of a fault, isolation is achieved by comparing the time scale at which the violation occurred to the time scale associated with a physical domain. In the final step, a Bayesian network is employed to describe the conditional dependence between faulty domains and fault signatures. Two examples are presented to demonstrate these concepts.