A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. (June 2015)

• Record Type: Journal Article
• Authors: Zhang, Xiaoyuan; Liang, Yitao; Zhou, Jianzhong; zang, Yi
• Is Part Of: Measurement. Volume 69(2015:Jun.)
• Page Start: 164
• Page End: 179
• Publication Date: 2015-06
• Subjects: Motor bearing -- Fault diagnosis -- Permutation entropy -- Ensemble empirical mode decomposition -- Support vector machine -- Inter-cluster distance; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2015.03.017
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The PE value of vibration signal is used to detect bearing faults. PE values of IMFs (IMF-PE) decomposed by EEMD are extracted as fault features. ICD in the feature space is used to optimize the parameter of SVM (ICDSVM). IMF-PE combined with ICDSVM is used to identify fault type and fault severity. The effectiveness of the proposed method is fully evaluated by experiments. Abstract: This paper presents a novel hybrid model for fault detection and classification of motor bearing. In the proposed model, permutation entropy (PE) of the vibration signal is calculated to detect the malfunctions of the bearing. If the bearing has faults, the vibration signal is decomposed into a set of intrinsic mode functions (IMFs) by ensemble empirical mode decomposition (EEMD). The PE values of the first several IMFs (IMF-PE) are calculate to reveal the multi-scale intrinsic characteristics of the vibration signal. Then, support vector machines (SVM) optimized by inter-cluster distance (ICD) in the feature space (ICDSVM) is used to classify the fault type as well as fault severity. Finally, the proposed model is fully evaluated by experiments and comparative studies. The results demonstrate its effectiveness and robustness for motor bearing fault detection and classification.