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Multiple Feature Vectors Based Fault Classification for WSN Integrated Bearing of Rolling Mill
المؤلفون المشاركون
Qin, Bo
Zhang, Luyang
Yin, Heng
Qin, Yan
المصدر
Journal of Control Science and Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-04-01
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
هندسة كهربائية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
For rolling mill machines, the operation status of bearing has a close relationship with process safety and production effectiveness.
Therefore, reliable fault diagnosis and classification are indispensable.
Traditional methods always characterize fault feature using a single fault vector, which may fail to reveal whole fault influences caused by complex process disturbances.
Besides, it may also lead to poor fault classification accuracy.
To solve the above-mentioned problems, a fault extraction method is put forward to extract multiple feature vectors and then a classification model is developed.
First, to collect sufficient data, a data acquisition system based on wireless sensor network is constructed to replace the traditional wired system which may bring dangers during production.
Second, the measured signal is filtered by a morphological average filtering algorithm to remove process noise and then the empirical mode decomposition method is applied to extract the intrinsic mode function (IMF) which contains the fault information.
On the basis of the IMFs, a time domain index (energy) and a frequency index (singular values) are proposed through Hilbert envelope analysis.
From the above analysis, the energy index and the singular value matrix are used for fault classification modeling based on the enhanced extreme learning machine (ELM), which is optimized by the bat algorithm to adjust the input weights and threshold of hidden layer node.
In comparison with the fault classification methods based on SVM and ELM, the experimental results show that the proposed method has higher classification accuracy and better generalization ability.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Qin, Bo& Zhang, Luyang& Yin, Heng& Qin, Yan. 2018. Multiple Feature Vectors Based Fault Classification for WSN Integrated Bearing of Rolling Mill. Journal of Control Science and Engineering،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1182931
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Qin, Bo…[et al.]. Multiple Feature Vectors Based Fault Classification for WSN Integrated Bearing of Rolling Mill. Journal of Control Science and Engineering No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1182931
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Qin, Bo& Zhang, Luyang& Yin, Heng& Qin, Yan. Multiple Feature Vectors Based Fault Classification for WSN Integrated Bearing of Rolling Mill. Journal of Control Science and Engineering. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1182931
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1182931
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
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