Research on an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty Fault Diagnosis Method for Tilting Pad Bearing of Rotating Equipment

Abstract

The research on fault diagnosis methods based on generative adversarial networks has achieved fruitful results, but most of the research objects are rolling bearings or gears, and the model test data are almost all derived from laboratory bench test data. In the industrial Internet environment, equipment-fault diagnosis is faced with the characteristics of large amounts of data, unbalanced data samples, and inconsistent data file lengths. Moreover, there are few research results on the fault diagnosis of rotor systems composed of shafts, impellers or blades, couplings, and tilting pad bearings. There are still shortcomings in the operational risk evaluation of rotor systems. In order to ensure the reliability and safety of rotor systems, an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty (IACWGAN-GP) model is constructed, a fault diagnosis method based on IACWGAN-GP for tilting pad bearings is proposed, and an intelligent fault diagnosis system platform for equipment in an industrial Internet environment is built. The verification results of engineering case data show that the fault diagnosis model based on IACWGAN-GP can adapt to any length of sequential data files, and the automatic identification accuracy of early faults in tilting pad bearings reaches 98.7%.