Rolling Bearing Fault Diagnosis Based on Stacked Autoencoder Network with Dynamic Learning Rate
المؤلفون المشاركون
Tang, Wei
Pan, Hong
Xu, Jin-Jun
Binama, Maxime
المصدر
Advances in Materials Science and Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-12-28
دولة النشر
مصر
عدد الصفحات
12
الملخص EN
Fault diagnosis is of great significance for ensuring the safety and reliable operation of rolling bearing in industries.
Stack autoencoder (SAE) networks have been widely applied in this field.
However, the model parameters such as learning rate are always fixed, which have an adverse effect on the convergence speed and accuracy of fault classification.
Thus, this paper proposes a dynamic learning rate adjustment approach for the stacked autoencoder network.
First, the input data is normalized and enhanced.
Second, the structure of the SAE network is selected.
According to the positive and negative value of the training error gradient, a learning rate reducing strategy is designed in order to be consistent with the current operation of the network.
Finally, the fault diagnosis models with different learning rate adjustment are conducted in order to validate the better performance of the proposed approach.
In addition, the influence of quantities of labeled sample data on the process of backpropagation is analyzed.
The results show that the proposed method can effectively increase the convergence speed and improve classification accuracy.
Moreover, it can reduce the labeled sample size and make the network more stable under the same classification accuracy.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Pan, Hong& Tang, Wei& Xu, Jin-Jun& Binama, Maxime. 2020. Rolling Bearing Fault Diagnosis Based on Stacked Autoencoder Network with Dynamic Learning Rate. Advances in Materials Science and Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1128871
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Pan, Hong…[et al.]. Rolling Bearing Fault Diagnosis Based on Stacked Autoencoder Network with Dynamic Learning Rate. Advances in Materials Science and Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1128871
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Pan, Hong& Tang, Wei& Xu, Jin-Jun& Binama, Maxime. Rolling Bearing Fault Diagnosis Based on Stacked Autoencoder Network with Dynamic Learning Rate. Advances in Materials Science and Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1128871
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1128871
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