Hierarchical Diagnostic Framework of Mechanical Seal Failure Modes Based on Maximum Lyapunov Exponent Anomaly Sensing and CatBoost Model Recognition
Centrifugal pump is widely used in the field of modern industrial production.Its opera-tion and health statement directly affect the energy consumption,efficiency and safety of the whole system.mechanical seal leakage or damage is one of the most typical failures of hydraulic rotat-ing machinery.The fault of pump equipment related to the mechanical seal failure directly affects the overall reliability and safety of the system.therefore,this paper studies a hierarchical diagnostic framework of mechanical seal failure modes based on maximum Lyapunov exponent anomaly sensing and CatBoost model recognition.Firstly,the maximum Lyapunov exponent of the vibration signal sequence collected at the mechanical seal is extracted,and the Type-1 fuzzy logic is designed based on the fuzzy statistical method and assignment method,so as to realize the abnormal detection and sensing of the mechanical seal fault.Then,once the mechanical seal abnormality is detected,the multi-scale fuzzy entropy is extracted from the original vibration signal and input into the catBoost model together with the maximum lyapunov exponent for mechanical seal failure pattern recogni-tion and diagnosis.Finally,the proposed hierarchical diagnosis framework is verified based on the real-world experimental data.The results show that the proposed approach achieves an anomaly detection accuracy of 100％and a failure mode recognition rate of 99.66％for seal failure,whose accuracy and robustness are better than those of intelligent models such as support vector machine,AdaBoost and deep neural network.
mechanical sealanomaly sensingfailure mode identificationmaximum Lyapunov exponentfuzzy formalismsCatBoost model
NETL
NSTL
万方数据
维普
