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Rolling Bearing Fault Detection Based on the Teager Energy Operator and Elman Neural Network
المؤلفون المشاركون
Liu, Hongmei
Wang, Jing
Lu, Chen
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2013، العدد 2013 (31 ديسمبر/كانون الأول 2013)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2013-05-20
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
This paper presents an approach to bearing fault diagnosis based on the Teager energy operator (TEO) and Elman neural network.
The TEO can estimate the total mechanical energy required to generate signals, thereby resulting in good time resolution and self-adaptability to transient signals.
These attributes reflect the advantage of detecting signal impact characteristics.
To detect the impact characteristics of the vibration signals of bearing faults, we used the TEO to extract the cyclical impact caused by bearing failure and applied the wavelet packet to reduce the noise of the Teager energy signal.
This approach also enabled the extraction of bearing fault feature frequencies, which were identified using the fast Fourier transform of Teager energy.
The feature frequencies of the inner and outer faults, as well as the ratio of resonance frequency band energy to total energy in the Teager spectrum, were extracted as feature vectors.
In order to avoid a frequency leak error, the weighted Teager spectrum around the fault frequency was extracted as feature vector.
These vectors were then used to train the Elman neural network and improve the robustness of the diagnostic algorithm.
Experimental results indicate that the proposed approach effectively detects bearing faults under variable conditions.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Liu, Hongmei& Wang, Jing& Lu, Chen. 2013. Rolling Bearing Fault Detection Based on the Teager Energy Operator and Elman Neural Network. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1031932
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Liu, Hongmei…[et al.]. Rolling Bearing Fault Detection Based on the Teager Energy Operator and Elman Neural Network. Mathematical Problems in Engineering No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-1031932
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Liu, Hongmei& Wang, Jing& Lu, Chen. Rolling Bearing Fault Detection Based on the Teager Energy Operator and Elman Neural Network. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1031932
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1031932
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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