A Signal Based “W” Structural Elements for Multi-scale Mathematical Morphology Analysis and Application to Fault Diagnosis of Rolling Bearings of Wind Turbines

作     者：Qiang Li Yong-Sheng Qi Xue-Jin Gao Yong-Ting Li Li-Qiang Liu Qiang Li;Yong-Sheng Qi;Xue-Jin Gao;Yong-Ting Li;Li-Qiang Liu

作者机构：Institute of Electric PowerInner Mongolia University of TechnologyHohhot 010080China Inner Mongolia Key Laboratory of Electrical and Mechanical ControlHohhot 010051China Faculty of InformationBeijing University of TechnologyBeijing 100124China 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2021年第18卷第6期

页      面：993-1006页

核心收录：

学科分类：080801[工学-电机与电器] 0808[工学-电气工程] 08[工学] 

基　　金：supported by National Natural Science Foundation of China (No. 61763037) Inner Mongolia Autonomous Region Natural Science Foundation of China(No. 2019LH06007) Science and Technology Plan Project of Inner Mongolia (No. 2019,2020GG028) 

主　　题：Fault diagnosis structural element multi-scale mathematical morphology rolling bearing correlation analysis 

摘      要：Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature extraction of wind turbine rolling bearings and to strengthen the feature information, a new structural element and an adaptive algorithm based on the peak energy are proposed,which are combined with spectral correlation analysis to form a fault diagnosis algorithm for wind turbine rolling bearings. The proposed method firstly addresses the problem of impulsive signal omissions that are prone to occur in the process of fault feature extraction of traditional structural elements and proposes a W structural element to capture more characteristic information. Then, the proposed method selects the scale of multi-scale mathematical morphology, aiming at the problem of multi-scale mathematical morphology scale selection and structural element expansion law. An adaptive algorithm based on peak energy is proposed to carry out morphological scale selection and structural element expansion by improving the computing efficiency and enhancing the feature extraction ***, the proposed method performs spectral correlation analysis in the frequency domain for an unknown signal of the extracted feature and identifies the fault based on the correlation coefficient. The method is verified by numerical examples using experimental rig bearing data and actual wind field acquisition data and compared with traditional triangular and flat structural elements. The experimental results show that the new structural elements can more effectively extract the pulses in the signal and reduce noise interference,and the fault-diagnosis algorithm can accurately identify the fault category and improve the reliability of the results.