Research on Fault Diagnosis Method of High-dimensional Complex Industrial Process based on SFFS-RBPCA
The reconstruction-based fault isolation method can suppress the influence of the smear effect and effectively reduce the false alarm rate.However,the computational cost of these methods will in-crease exponentially with the system dimension as well as the number of fault variables,making it chal-lenging to directly apply in the real-time fault diagnosis of high-dimensional complex industrial process.Therefore,a new fault diagnosis method integrating the sequential floating forward selection aided recon-struction-based principal component analysis(SFFS-RBPCA)was proposed,the PCA monitoring model was established based on historical samples,and the combined index was used to detect the faults of real-time data.Then,the sequence feature selection method was introduced to locate fault variables in the fault isolation process.Furthermore,a simulation example and a practical industrial case were employed to verify the diagnostic performance of the proposed method.The results show that the proposed method can ensure a high fault detection rate and a low false alarm rate by consuming a small amount of computa-tion,achieving a good balance between diagnostic accuracy and diagnostic efficiency.The proposed method can effectively deal with the complex faults of high-dimensional systems and meet the online diag-nosis requirements.
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