Bearing fault recognition based on wavelet frequency band division and multi-level clustering_21_program_code

It is the key problem of machine condition monitoring to judge whether the rolling bearing has a fault or not and judge the fault location according to the noise signal. Aiming at this problem, a rolling bearing fault identification method is proposed based on Wavelet Frequency Band Subdivision (WFBS), Principal Component Analysis (PCA) and Multi-Level Clustering (MLC). Firstly, the original signals are divided into different Wavelet Packet Energy Spectrum indicators based on wavelet frequency bands, then they are combined with time-frequency domain indicators to form a parameter set. After PCA processing, the dimension of the parameter set is reduced to an appropriate dimension and the useful information is retained as much as possible. In this paper, the concept of WFBS and MLC are proposed to deal with the parameter set. The fault location and fault severity are determined in the form of confusion matrix graph through MLC. The experimental results show that this method can identify different fault types of rolling bearings with high accuracy and strong applicability, and displays practical significance in engineering applications.