Health Status Recognition of Rotating Machinery Based on Deep Residual Shrinkage Network Under Time-Varying Conditions

Abstract

Currently, the research on the health state of rotating machinery under time-varying operating conditions mainly focuses on using a combination of several constant operating conditions or uniformly changing speed and load. This article studied the health status recognition of rotating machinery under nonlinear and continuous changes in speed and load. A health status recognition method of rotating machinery was proposed based on the gram angle field and deep residual contraction network. Considering the influence of working conditions on signal characteristics, the speed, load, and multidimensional time-domain features are fused to form feature vectors. The feature vectors were transformed into images by gram coding. The color contrast relationship mapped from the overall difference distribution of sample feature indexes to the image was not changed while the feature timing was retained, which weakened the influence of working condition information on the sample state, improved the deep residual shrinkage network (DRSN) structure, and introduced the Gaussian error linear unit (GELU) activation function. The experimental verification is completed on the reducer experimental platform and the Xi’an Jiaotong University (XJTU)-Changxing Sumyoung Technology Company Ltd. (XJTU-SY) dataset. The results show that the method can effectively identify the health state of rotating machinery under time-varying working conditions.