Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection
المؤلفون المشاركون
Wu, Shoupeng
Ding, Enjie
Dong, Fei
Fan, Chunyang
Huang, Yanqiu
Cheng-hua, Huang
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-29، 29ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-03-26
دولة النشر
مصر
عدد الصفحات
29
التخصصات الرئيسية
الملخص EN
In order to enhance the performance of bearing fault diagnosis and classification, features extraction and features dimensionality reduction have become more important.
The original statistical feature set was calculated from single branch reconstruction vibration signals obtained by using maximal overlap discrete wavelet packet transform (MODWPT).
In order to reduce redundancy information of original statistical feature set, features selection by adjusted rand index and sum of within-class mean deviations (FSASD) was proposed to select fault sensitive features.
Furthermore, a modified features dimensionality reduction method, supervised neighborhood preserving embedding with label information (SNPEL), was proposed to realize low-dimensional representations for high-dimensional feature space.
Finally, vibration signals collected from two experimental test rigs were employed to evaluate the performance of the proposed procedure.
The results show that the effectiveness, adaptability, and superiority of the proposed procedure can serve as an intelligent bearing fault diagnosis system.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Dong, Fei& Cheng-hua, Huang& Ding, Enjie& Wu, Shoupeng& Fan, Chunyang& Huang, Yanqiu. 2018. Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection. Shock and Vibration،Vol. 2018, no. 2018, pp.1-29.
https://search.emarefa.net/detail/BIM-1215297
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Dong, Fei…[et al.]. Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection. Shock and Vibration No. 2018 (2018), pp.1-29.
https://search.emarefa.net/detail/BIM-1215297
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Dong, Fei& Cheng-hua, Huang& Ding, Enjie& Wu, Shoupeng& Fan, Chunyang& Huang, Yanqiu. Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection. Shock and Vibration. 2018. Vol. 2018, no. 2018, pp.1-29.
https://search.emarefa.net/detail/BIM-1215297
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1215297
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