Fault Relevant Variable Selection for Fault Diagnosis

In process monitoring, fault relevant variable selection and fault diagnosis are two important branches. But they are often discussed independently and scarcely integrated in research. To integrate them, a novel fault diagnosis algorithm based on fault relevant variable selection is proposed in this paper. The main contents are summarized as follows. For fault relevant variable selection, from normal state to fault state, the relative changes between variables and statistics are analyzed using least absolute shrinkage and selection operator (LASSO). In order to determine the optimal set of fault relevant variable, a fault reconstruction algorithm based on least angle regression (LARS) is proposed. The set of relevant variables is constantly updated until there is no abnormality after reconstruction. Finally, a monitoring strategy based on fault subspace is proposed. It can detect fault effectively and provide useful information for fault diagnosis. The effectiveness of the proposed algorithm is illustrated by some experiments.

Abstract licence: CC BY-NC-ND

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