Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions
المؤلفون المشاركون
Wei, Li
Tong, Zhe
Zhang, Bo
Zhang, Meng
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-06-28
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Bearing failure is the most common failure mode in rotating machinery and can result in large financial losses or even casualties.
However, complex structures around bearing and actual variable working conditions can lead to large distribution difference of vibration signal between a training set and a test set, which causes the accuracy-dropping problem of fault diagnosis.
Thus, how to improve efficiently the performance of bearing fault diagnosis under different working conditions is always a primary challenge.
In this paper, a novel bearing fault diagnosis under different working conditions method is proposed based on domain adaptation using transferable features(DATF).
The datasets of normal bearing and faulty bearings are obtained through the fast Fourier transformation (FFT) of raw vibration signals under different motor speeds and load conditions.
Then we reduce marginal and conditional distributions simultaneously across domains based on maximum mean discrepancy (MMD) in feature space by refining pseudo test labels, which can be obtained by the nearest-neighbor (NN) classifier built on training data, and then a robust transferable feature representation for training and test domains is achieved after several iterations.
With the help of the NN classifier trained on transferable features, bearing fault categories are identified accurately in final.
Extensive experiment results show that the proposed method under different working conditions can identify the bearing faults accurately and outperforms obviously competitive approaches.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Tong, Zhe& Wei, Li& Zhang, Bo& Zhang, Meng. 2018. Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions. Shock and Vibration،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1215394
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Tong, Zhe…[et al.]. Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions. Shock and Vibration No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1215394
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Tong, Zhe& Wei, Li& Zhang, Bo& Zhang, Meng. Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions. Shock and Vibration. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1215394
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1215394
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