Structured Sparsity Modeling for Improved Multivariate Statistical Analysis based Fault Isolation
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In order to improve the fault diagnosis capability of multivariate statistical methods, this article introduces a fault isolation method based on structured sparsity modelling. The developed method relies on the reconstruction based contribution analysis and the process structure information can be incorporated into the reconstruction objective function in the form of structured sparsity regularization terms. The structured sparsity terms allow optimal selection of fault variables over structures like blocks or networks of process variables, hence more accurate fault isolation can be achieved. Four structured sparsity terms corresponding to different kinds of process information are considered, namely, partially known sparse support, block sparsity, clustered sparsity and tree-structured sparsity. The optimization problems involving the structured sparsity terms can be solved using the Alternating Multiplier Method (ADMM) algorithm, which is fast and efficient. In addition, the ADMM algorithm can be easily extended in a parallel/distributed way to handle large-scale systems with a large number of variables. Through a simulation example and an application study to a coal-fired power plant, it is verified that the proposed method can better isolate faulty variables by incorporating process structure information.
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