Improvement of Adaptive GAs and Back Propagation ANNs Performance in Condition Diagnosis of Multiple Bearing System Using Grey Relational Analysis
المؤلفون المشاركون
Wulandhari, Lili A.
Wibowo, Antoni
Desa, Mohammad I.
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2014-12-17
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Condition diagnosis of multiple bearings system is one of the requirements in industry field, because bearings are used in many equipment and their failure can result in total breakdown.
Conditions of bearings commonly are reflected by vibration signals data.
In multiple bearing condition diagnosis, it will involve many types of vibration signals data; thus, consequently, it will involve many features extraction to obtain precise condition diagnosis.
However, large number of features extraction will increase the complexity of the diagnosis system.
Therefore, in this paper, we presented a diagnosis method which is hybridization of adaptive genetic algorithms (AGAs), back propagation neural networks (BPNNs), and grey relational analysis (GRA) to diagnose the condition of multiple bearings system.
AGAs are used in the diagnosis algorithm to determine the best initial weights of BPNNs in order to improve the diagnosis accuracy.
In addition, GRA is applied to determine and select the dominant features from the vibration signal data which will provide good diagnosis of multiple bearings system in less features extraction.
The experiments results show that AGAs-BPNNs with GRA approaches can increase the accuracy of diagnosis in shorter processing time, compared with the AGAs-BPNNs without the GRA.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wulandhari, Lili A.& Wibowo, Antoni& Desa, Mohammad I.. 2014. Improvement of Adaptive GAs and Back Propagation ANNs Performance in Condition Diagnosis of Multiple Bearing System Using Grey Relational Analysis. Computational Intelligence and Neuroscience،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1016727
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wulandhari, Lili A.…[et al.]. Improvement of Adaptive GAs and Back Propagation ANNs Performance in Condition Diagnosis of Multiple Bearing System Using Grey Relational Analysis. Computational Intelligence and Neuroscience No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1016727
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wulandhari, Lili A.& Wibowo, Antoni& Desa, Mohammad I.. Improvement of Adaptive GAs and Back Propagation ANNs Performance in Condition Diagnosis of Multiple Bearing System Using Grey Relational Analysis. Computational Intelligence and Neuroscience. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1016727
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1016727
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