Research on Data-driven Clustering Analysis Fault Identification Method

doi:10.3901/JME.2020.18.007

Abstract

Abstract: Equipment fault prediction and health management have entered the era of intelligence. It is the premise to realize fault self-recovery regulation by analysing and extracting fault characteristic information from equipment condition monitoring data to realize automatic fault identification and classification.The centrifugal compressor is taken as the research object. Based on K-means clustering analysis and data-driven method, the K-means clustering fault pattern recognition algorithm model is constructed. The fault characteristic parameters with high sensitivity of fault identification are selected as model input parameters, and the clustering centers of different faults are obtained by monitoring data training, and the data-driven centrifugal compressor fault identification method based on the cluster center distance criterion is explored. The model is validated by the normal and surge, rubbing and misalignment case data of a centrifugal compressor of CNPC. The results show that the data-driven clustering fault identification method can realize the automatic identification of multi-fault mode of centrifugal compressor; compared with the support vector machine fault identification method, the K-means clustering analysis fault identification method is more accurate; the data-driven method calculates the clustering center of real-time monitoring data, and uses the intuitive distance criterion to realize automatic identification of fault modes, which can realize fault diagnosis and intelligent decision-making, and provide technical support for self-healing regulation.

Key words: data-driven, clustering analysis, fault identification, self-recovery regulation, fault characteristic parameters

CLC Number:

TH17

WANG Qingfeng, LIU Jiahe, WEI Bingkun, ZHANG Cheng. Research on Data-driven Clustering Analysis Fault Identification Method[J]. Journal of Mechanical Engineering, 2020, 56(18): 7-14.

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