Bearing Fault Detection by One-Dimensional Convolutional Neural Networks
المؤلف
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-07-27
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Bearing faults are the biggest single source of motor failures.
Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults.
The typical decision support systems require feature extraction and classification as two distinct phases.
Extracting fixed features each time may require a significant computational cost preventing their use in real-time applications.
Furthermore, the selected features for the classification phase may not represent the most optimal choice.
In this paper, the use of 1D Convolutional Neural Networks (CNNs) is proposed for a fast and accurate bearing fault detection system.
The feature extraction and classification phases of the bearing fault detection are combined into a single learning body with the implementation of 1D CNN.
The raw vibration data (signal) is fed into the proposed system as input eliminating the need for running a separate feature extraction algorithm each time vibration data is analyzed for classification.
Implementation of 1D CNNs results in more efficient systems in terms of computational complexity.
The classification performance of the proposed system with real bearing data demonstrates that the reduced computational complexity is achieved without a compromise in fault detection accuracy.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Eren, Levent. 2017. Bearing Fault Detection by One-Dimensional Convolutional Neural Networks. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1192443
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Eren, Levent. Bearing Fault Detection by One-Dimensional Convolutional Neural Networks. Mathematical Problems in Engineering No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1192443
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Eren, Levent. Bearing Fault Detection by One-Dimensional Convolutional Neural Networks. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1192443
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1192443
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