کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
729737 1461518 2015 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله
A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM
چکیده انگلیسی


• 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.

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Measurement - Volume 69, June 2015, Pages 164–179
نویسندگان
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