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Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance
المؤلفون المشاركون
Lai, Z. H.
Wang, S. B.
Zhang, G. Q.
Zhang, C. L.
Zhang, J. W.
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-03-10
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
The weak-signal detection technologies based on stochastic resonance (SR) play important roles in the vibration-based health monitoring and fault diagnosis of rolling bearings, especially at their early-fault stage.
Aiming at the parameter-fixed vibration signals in practical engineering, it is feasible to diagnose the potential rolling bearing faults through adaptively adjusting the SR system parameters, as well as other generalized parameters such as the amplitude-transformation coefficient and scale-transformation coefficient.
However, extant adaptive adjustment methods focus on the system parameters, while the adjustments of other adjustable parameters have not been fully studied, thus limiting the detection performance of the adaptive SR method.
In order to further enhance the detection performance of adaptive SR methods and extend their application in rolling bearing fault diagnosis, an adaptive multiparameter-adjusting SR (AMPASR) method for bistable systems based on particle swarm optimization (PSO) algorithm is proposed in this paper.
This method can produce optimal SR output through adaptively adjusting multiparameters, thus realizing fault feature extraction and further fault diagnosis.
Furthermore, the influence of algorithm parameters on the optimization results is discussed, and the optimization results of the Langevin system and the Duffing system are compared.
Finally, we propose a weak-signal detection method based on the AMPASR of the Duffing system and employ three diagnosis examples involving inner ring fault, outer ring fault, and rolling element fault diagnoses to demonstrate its feasibility in rolling bearing fault diagnosis.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Lai, Z. H.& Wang, S. B.& Zhang, G. Q.& Zhang, C. L.& Zhang, J. W.. 2020. Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance. Shock and Vibration،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1210098
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Lai, Z. H.…[et al.]. Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance. Shock and Vibration No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1210098
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Lai, Z. H.& Wang, S. B.& Zhang, G. Q.& Zhang, C. L.& Zhang, J. W.. Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1210098
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1210098
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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