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Intelligent Mechanical Fault Diagnosis Based on Multiwavelet Adaptive Threshold Denoising and MPSO
المؤلفون المشاركون
Li, Ke
Chen, Peng
Cao, Yi
Wang, Hua-Qing
Sun, Hao
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2014-07-22
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
The condition diagnosis of rotating machinery depends largely on the feature analysis of vibration signals measured for the condition diagnosis.
However, the signals measured from rotating machinery usually are nonstationary and nonlinear and contain noise.
The useful fault features are hidden in the heavy background noise.
In this paper, a novel fault diagnosis method for rotating machinery based on multiwavelet adaptive threshold denoising and mutation particle swarm optimization (MPSO) is proposed.
Geronimo, Hardin, and Massopust (GHM) multiwavelet is employed for extracting weak fault features under background noise, and the method of adaptively selecting appropriate threshold for multiwavelet with energy ratio of multiwavelet coefficient is presented.
The six nondimensional symptom parameters (SPs) in the frequency domain are defined to reflect the features of the vibration signals measured in each state.
Detection index (DI) using statistical theory has been also defined to evaluate the sensitiveness of SP for condition diagnosis.
MPSO algorithm with adaptive inertia weight adjustment and particle mutation is proposed for condition identification.
MPSO algorithm effectively solves local optimum and premature convergence problems of conventional particle swarm optimization (PSO) algorithm.
It can provide a more accurate estimate on fault diagnosis.
Practical examples of fault diagnosis for rolling element bearings are given to verify the effectiveness of the proposed method.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Sun, Hao& Li, Ke& Wang, Hua-Qing& Chen, Peng& Cao, Yi. 2014. Intelligent Mechanical Fault Diagnosis Based on Multiwavelet Adaptive Threshold Denoising and MPSO. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-15.
https://search.emarefa.net/detail/BIM-449144
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Sun, Hao…[et al.]. Intelligent Mechanical Fault Diagnosis Based on Multiwavelet Adaptive Threshold Denoising and MPSO. Mathematical Problems in Engineering No. 2014 (2014), pp.1-15.
https://search.emarefa.net/detail/BIM-449144
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Sun, Hao& Li, Ke& Wang, Hua-Qing& Chen, Peng& Cao, Yi. Intelligent Mechanical Fault Diagnosis Based on Multiwavelet Adaptive Threshold Denoising and MPSO. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-15.
https://search.emarefa.net/detail/BIM-449144
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-449144
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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