Research on an Improved Wasserstein Generative Adversarial Network Early Fault Warning Method for Rotating Machinery

Abstract

Early fault warning for large-scale high-speed rotating machinery can effectively reduce unplanned downtime and avoid major safety accidents. Aiming at the problems of difficult screening of multi-source common sensitive features, the challenging training of neural networks with a small number of sensitive features, and the difficulty of directly using generative adversarial networks for early fault warning, this paper constructs an early fault warning model based on multi-source common sensitive features and an improved Wasserstein generative adversarial network, proposing an early fault warning method for rotating machinery. The model was verified by using the open XJTU-SY bearing laboratory data, the P3409A centrifugal pump bearing fault engineering case data of a petrochemical company and the rotor system engineering case data of a circulating hydrogen centrifugal compressor of a petrochemical company. The early fault warning method of rotating machinery proposed in this paper warns the bearing fault of centrifugal pump 160 hours in advance and the rotor system fault of centrifugal compressor 1330 minutes in advance. Compared with the two published methods, the proposed method has better early fault warning effect, better normal and abnormal health index discrimination and less false warning.

Keywords

• Rotating machines
• deep learning
• generative adversarial networks
• early fault warning