کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4946333 1439285 2017 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection
ترجمه فارسی عنوان
یک روش هوشمند جدید برای تشخیص گسل های مبتنی بر خوشه بندی انتشار متفاوتی و انتخاب ویژگی های تطبیقی
کلمات کلیدی
انتخاب ویژگی سازگاری توزیع وابستگی، غلتک عنصر بلبرینگ، تشخیص خطا هوشمند،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Bearings faults are one of the main causes of breakdown of rotating machines. Thus detection and diagnosis of mechanical faults in bearings is very crucial for the reliable operation. A novel intelligent fault diagnosis method for roller bearings based on affinity propagation (AP) clustering algorithm and adaptive feature selection technique is proposed to better equip with a non-expert to carry out diagnosis operations. Ensemble empirical mode decomposition (EEMD) and wavelet packet transform (WPT) are utilized to accurately extract the fault characteristic information buried in the vibration signals. Moreover, in order to improve the efficiency of clustering algorithm and avoid the curse of dimensionality, a new adaptive features selection technique is developed in this work, whose effectiveness is verified in comparison with other methods. The proposed intelligent method is then applied to the bearing fault diagnosis. Results demonstrate that the proposed method is able to reliably and accurately identify different fault categories and severities of bearings.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 116, 15 January 2017, Pages 1-12
نویسندگان
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