Remaining Useful Life Prediction of Rolling Bearings Using PSR, JADE, and Extreme Learning Machine
المؤلفون المشاركون
Lu, Siliang
Liu, Fang
Liu, Yongbin
Zhao, Jiwen
He, Bing
Zhao, Yilei
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-04-18
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Rolling bearings play a pivotal role in rotating machinery.
The degradation assessment and remaining useful life (RUL) prediction of bearings are critical to condition-based maintenance.
However, sensitive feature extraction still remains a formidable challenge.
In this paper, a novel feature extraction method is introduced to obtain the sensitive features through phase space reconstitution (PSR) and joint with approximate diagonalization of Eigen-matrices (JADE).
Firstly, the original features are extracted from bearing vibration signals in time and frequency domain.
Secondly, the PSR is applied to embed the original features into high dimensional phase space.
The between-class and within-class scatter ( S S ) are calculated to evaluate the feature sensitivity through the phase point distribution of different degradation stages and then different weights are assigned to the corresponding features based on the calculated S S .
Thirdly, the JADE is employed to fuse the weighted features to obtain the advanced features which can better reflect the bearing degradation process.
Finally, the advanced features are input into the extreme learning machine (ELM) to train the RUL prediction model.
A set of experimental case studies are carried out to verify the effectiveness of the proposed method.
The results show that the extracted advanced features can better reflect the degradation process compared to traditional features and could effectively predict the RUL of bearing.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Liu, Yongbin& He, Bing& Liu, Fang& Lu, Siliang& Zhao, Yilei& Zhao, Jiwen. 2016. Remaining Useful Life Prediction of Rolling Bearings Using PSR, JADE, and Extreme Learning Machine. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1112744
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Liu, Yongbin…[et al.]. Remaining Useful Life Prediction of Rolling Bearings Using PSR, JADE, and Extreme Learning Machine. Mathematical Problems in Engineering No. 2016 (2016), pp.1-13.
https://search.emarefa.net/detail/BIM-1112744
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Liu, Yongbin& He, Bing& Liu, Fang& Lu, Siliang& Zhao, Yilei& Zhao, Jiwen. Remaining Useful Life Prediction of Rolling Bearings Using PSR, JADE, and Extreme Learning Machine. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1112744
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1112744
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