文献类型：会议论文

英文题名：Shaft Misalignment Fault Feature Extraction and Diagnosis via MCSA Utilizing Empirical Principal Component Analysis

作者：Liao, Zhiqiang[1]; Huang, Zhende[1]; Song, Xuewei[1]; Jia, Baozhu[1]; Liang, Guanlong[1]; Li, Xiaoyu[1]

机构：[1] Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang, China

会议论文集：ICSMD 2024 - 5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence

会议日期：October 31, 2024 - November 3, 2024

会议地点：No.139 Qiyun Avenue, Tunxi District, Mount Huangshan, Huangshan, China

语种：英文

外文关键词：Empirical mode decomposition - Fault detection - High pass filters - Image coding - Image compression - Image segmentation - Image thinning - Phase locked loops

外文摘要：The fault features of shaft misalignment in stator currents are often suppressed and masked by interference and noise, leading to weak fault features and affecting the accuracy of fault diagnosis. This paper proposes a feature extraction and diagnosis method for motor shaft misalignment through motor current signature analysis (MCSA) based on empirical principal element. The designed power frequency filtering technique is first applied to diminish the dominance of the power frequency in the signal spectrum, thereby improving the representation of other harmonics features. Subsequently, empirical principal component analysis (EPCA) is employed to extract fault features from the current signal indicative of shaft misalignment. The shaft misalignment faults diagnosis is achieved by comparing with the theoretical fault frequency associated with shaft misalignment. The proposed method was validated using the data from motor experimental platform, and was compared with empirical mode decomposition, high-pass filtering, and principal component analysis method. The results confirm the feasibility and effectiveness of the proposed method. ? 2024 IEEE.