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• W2122820732 abstract "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." @default.
• W2122820732 created "2016-06-24" @default.
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• W2122820732 date "2011-01-01" @default.
• W2122820732 modified "2023-10-18" @default.
• W2122820732 title "Effective fault detection and isolation using bond graph-based domain decomposition" @default.
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• W2122820732 doi "https://doi.org/10.1016/j.compchemeng.2010.07.033" @default.
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