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Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors
المؤلفون المشاركون
Islam, Md. Rashedul
Khan, Sheraz Ali
Kim, Jong-Myon
المصدر
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2015-12-21
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
Optimal feature distribution and feature selection are of paramount importance for reliable fault diagnosis in induction motors.
This paper proposes a hybrid feature selection model with a novel discriminant feature distribution analysis-based feature evaluation method.
The hybrid feature selection employs a genetic algorithm- (GA-) based filter analysis to select optimal features and a k-NN average classification accuracy-based wrapper analysis approach that selects the most optimal features.
The proposed feature selection model is applied through an offline process, where a high-dimensional hybrid feature vector is extracted from acquired acoustic emission (AE) signals, which represents a discriminative fault signature.
The feature selection determines the optimal features for different types and sizes of single and combined bearing faults under different speed conditions.
The effectiveness of the proposed feature selection scheme is verified through an online process that diagnoses faults in an unknown AE fault signal by extracting only the selected features and using the k-NN classification algorithm to classify the fault condition manifested in the unknown signal.
The classification performance of the proposed approach is compared with those of existing state-of-the-art average distance-based approaches.
Our experimental results indicate that the proposed approach outperforms the existing methods with regard to classification accuracy.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Islam, Md. Rashedul& Khan, Sheraz Ali& Kim, Jong-Myon. 2015. Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors. Journal of Sensors،Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1110585
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Islam, Md. Rashedul…[et al.]. Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors. Journal of Sensors No. 2016 (2016), pp.1-16.
https://search.emarefa.net/detail/BIM-1110585
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Islam, Md. Rashedul& Khan, Sheraz Ali& Kim, Jong-Myon. Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors. Journal of Sensors. 2015. Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1110585
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1110585
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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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
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