Xiong Ruolan, Liu Aihua, Xu Dongfang, Qu Chunyang, Wu Yulong
School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, China.
Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572024, China.
Sensors (Basel). 2024 Dec 4;24(23):7769. doi: 10.3390/s24237769.
Under heavy load conditions, bearings are subjected to non-uniform and frequently changing loads, which leads to randomness in the spatial distribution of bearing degradation characteristics. Aiming at the problem that the traditional degradation index cannot accurately reflect the degradation state of heavy-duty bearings in the whole life cycle, a new degradation evaluation method based on multi-domain features is proposed in this paper, which aims to capture the early degradation point of heavy-duty bearings and characterize their degradation trend. Firstly, the energy entropy feature is obtained by improving the wavelet packet decomposition, and the original multi-domain feature set is constructed by combining the time domain and frequency domain features. Then, the optimal feature matrix is formed by using the comprehensive evaluation index. Finally, integrating probability and distance information, a comprehensive degradation index was constructed to evaluate the degradation, determine the initial degradation time, and quantitatively analyze the bearing degradation state. The validity of the proposed method is verified in two datasets. The proposed method can accurately identify the early degradation of bearings and track the state of bearing degradation, so as to realize the degradation assessment.
