Multi-dimensional Fault Diagnosis Using a Subspace Approach
نویسندگان
چکیده
Fault detection and process monitoring using principal component analysis (PCA) have been studied intensively and applied to industrial processes. PCA is used to deene an orthogonal partition of the measurement space into two orthogonal subspaces: a principal component sub-space (PCS), and a residual subspace (RS). In this paper, each process fault is also described by a subspace and the fundamental issues of fault detectability, reconstructabil-ity, identiiability and isolatability are analyzed. Based on the fault subspace, fault magnitude and the PCS, necessary and suucient conditions are provided to determine if the faults from a simulated process are detectable, recon-structable, identiiable and isolatable.
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