A Novel Fault Diagnosis Model for Bearing of Railway Vehicles Using Vibration Signals Based on Symmetric Alpha-Stable Distribution Feature Extraction
المؤلفون المشاركون
Xiong, Qing
Zhang, Weihua
Lu, Tianwei
Mei, Guiming
Li, Yongjian
المصدر
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-12-20
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Axle box bearings are the most critical mechanical components of railway vehicles.
Condition monitoring is of great benefit to ensure the healthy status of bearings in the railway train.
In this paper, a novel fault diagnosis model for axle box bearing based on symmetric alpha-stable distribution feature extraction and least squares support vector machines (LS-SVM) using vibration signals is proposed which is conducted in three main steps.
Firstly, fast nonlocal means is used for denoising and ensemble empirical mode decomposition is applied to extract fault feature information.
Then a new statistical method of feature extraction, symmetric alpha-stable distribution, is employed to obtain representative features from intrinsic mode functions.
Additionally, the hybrid fault feature sets are input into LS-SVM to identify the fault type.
To enhance the performance of LS-SVM in the case of small-scale samples, Morlet wavelet kernel function is combined with LS-SVM for the classification of fault type and fault severity and the particle swarm optimization is used for the optimization of LS-WSVM parameters.
Finally, the experimental results demonstrate that the proposed approach performs more effectively and robustly than the other methods in small-scale samples for fault detection and classification of railway vehicle bearings.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Li, Yongjian& Zhang, Weihua& Xiong, Qing& Lu, Tianwei& Mei, Guiming. 2016. A Novel Fault Diagnosis Model for Bearing of Railway Vehicles Using Vibration Signals Based on Symmetric Alpha-Stable Distribution Feature Extraction. Shock and Vibration،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1119346
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Li, Yongjian…[et al.]. A Novel Fault Diagnosis Model for Bearing of Railway Vehicles Using Vibration Signals Based on Symmetric Alpha-Stable Distribution Feature Extraction. Shock and Vibration No. 2016 (2016), pp.1-13.
https://search.emarefa.net/detail/BIM-1119346
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Li, Yongjian& Zhang, Weihua& Xiong, Qing& Lu, Tianwei& Mei, Guiming. A Novel Fault Diagnosis Model for Bearing of Railway Vehicles Using Vibration Signals Based on Symmetric Alpha-Stable Distribution Feature Extraction. Shock and Vibration. 2016. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1119346
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1119346
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