Intelligent expert system fault diagnosis based on PCA–SPE–CNN classifier

Abstract

Centrifugal compressors are widely used in the oil and natural gas industry for gas compression, reinjection, and transportation, To address the challenges of the difficult separation of data anomalies from equipment failures and limited knowledge acquisition from expert knowledge bases, this article proposes a dynamic fault diagnosis method for centrifugal compressor expert systems, combining convolutional neural networks (CNN) and principal component analysis for statistical process monitoring (PCA-SPE). Realize the combination of expert knowledge and instrument data, and break through the weak links in existing petrochemical instrument safety monitoring technology and traditional expert systems. The method has been validated using process data from centrifugal compressors. The results demonstrate that the method achieved 100% classification accuracy for two types of faults: non-starting of the drive machine and excessively low oil pressure. Combined with the expert system, it reached a satisfactory diagnostic performance.

Affiliated Institutions

• Zhoukou Normal University CN
• Jilin University of Chemical Technology CN

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