Bearing Fault Diagnosis Based on the Switchable Normalization SSGAN with 1-D Representation of Vibration Signals as Input

Bearing Fault Diagnosis Based on the Switchable Normalization SSGAN with 1-D Representation of Vibration Signals as Input

Blog Article

The bearing is a component of the support shaft that guides the BRONZE MAN HONEY rotational movement of the shaft, widely used in the mechanical industry and also called a mechanical joint.In bearing fault diagnosis, the accuracy much depends on the feature extraction, which always needs a lot of training samples and classification in the commonly used methods.Neural networks are good at latent feature extraction and fault classification, however, they have problems with instability and over-fitting, and more labeled samples must be trained.Switchable normalization and semi-supervised learning are introduced to solve the above obstacles in this paper, which proposes a novel bearing fault diagnosis method based on switchable normalization semi-supervised generative adversarial networks (SN-SSGAN) with 1-dimensional representation of vibration signals as input.Experimental results showed that the proposed method has a desirable 99.

93% classification accuracy in the case of less labeled data from the public data Truck Model Kit set of West Reserve University, which is better than the state-of-the-art methods.