Fault Detection and Behavior Analysis of Wheelset Bearing Using Adaptive Convolutional Sparse Coding Technique Combined with Bandwidth Optimization
المؤلفون المشاركون
He, Liu
Yi, Cai
Tan, Andy C.C.
Lin, Jianhui
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-27، 27ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-18
دولة النشر
مصر
عدد الصفحات
27
التخصصات الرئيسية
الملخص EN
Wheelset bearing is a critical and easily damaged component of a high-speed train.
Wheelset bearing fault diagnosis is of great significance to ensure safe operation of high-speed trains and realize intelligent operation and maintenance.
The convolutional sparse coding technique based on the dictionary learning algorithm (CSCT-DLA) provides an effective algorithm framework for extracting the impulses caused by bearing defect.
However, dictionary learning is easily affected by foundation vibration and harmonic interference and cannot learn the key structure related to fault impulses.
At the same time, the detection performance of fault impulse heavily depends on the selection of parameters in this approach.
Union of convolutional dictionary learning algorithm (UC-DLA) is an efficient algorithm in CSCT-DLA.
In this paper, UC-DLA is introduced and improved for wheelset bearing fault detection.
Finally, a novel bearing fault detection method, adaptive UC-DLA combined with bandwidth optimization (AUC-DLA-BO), is proposed.
The mathematical formulation of AUC-DLA-BO is a sort of constrained optimization problem, which can overcome foundation vibration and harmonic interference and adaptively determine parameters related to UC-DLA.
The proposed method can detect the fault resonance band adaptively, eliminate the noise with the same frequency band as the fault resonance band, and highlight the bearing fault impulses.
Simulated signals and bench tests are used to verify the effectiveness of the proposed method.
The results show that AUC-DLA-BO can effectively detect bearing faults and realize the refined analysis of fault behavior.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
He, Liu& Yi, Cai& Lin, Jianhui& Tan, Andy C.C.. 2020. Fault Detection and Behavior Analysis of Wheelset Bearing Using Adaptive Convolutional Sparse Coding Technique Combined with Bandwidth Optimization. Shock and Vibration،Vol. 2020, no. 2020, pp.1-27.
https://search.emarefa.net/detail/BIM-1213129
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
He, Liu…[et al.]. Fault Detection and Behavior Analysis of Wheelset Bearing Using Adaptive Convolutional Sparse Coding Technique Combined with Bandwidth Optimization. Shock and Vibration No. 2020 (2020), pp.1-27.
https://search.emarefa.net/detail/BIM-1213129
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
He, Liu& Yi, Cai& Lin, Jianhui& Tan, Andy C.C.. Fault Detection and Behavior Analysis of Wheelset Bearing Using Adaptive Convolutional Sparse Coding Technique Combined with Bandwidth Optimization. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-27.
https://search.emarefa.net/detail/BIM-1213129
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1213129
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر