Abstract：In order to improve the running efficiency of rotating machinery and to identify its potential failure, a fault classification method based on the multi-feature extraction and improved Mahalanobis Taguchi system (MTS) was proposed.The methods of time domain, frequency domain and the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) were combinedly used to obtain a multi-dimensional feature set.Integrating the advantages of the MTS and the directed acyclic graph (DAG), a DAG-MTS multi-class classification model was constructed and applied to bearing fault diagnosis.The effectiveness and applicability of the model was verified by using experimental data.The results show that the model can quickly and accurately identify the fault of rolling bearings.
彭宅铭，程龙生，詹君，姚启峰. 基于多特征提取和改进马田系统的滚动轴承故障分类方法研究[J]. 振动与冲击, 2020, 39(6): 249-256.
PENG Zhaiming, CHENG Longsheng, ZHAN Jun, YAO Qifeng. Fault classification method for rolling bearings based on the multi-featureextraction and modified Mahalanobis-Taguchi system. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(6): 249-256.
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