Deep Adaptive Adversarial Network-Based Method for Mechanical Fault Diagnosis under Different Working Conditions
المؤلفون المشاركون
Jiang, Xingxing
Wang, Jinrui
Ji, Shanshan
Han, Baokun
Bao, Huaiqian
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-07-23
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
The demand for transfer learning methods for mechanical fault diagnosis has considerably progressed in recent years.
However, the existing methods always depend on the maximum mean discrepancy (MMD) in measuring the domain discrepancy.
But MMD can not guarantee the different domain features to be similar enough.
Inspired by generative adversarial networks (GAN) and domain adversarial training of neural networks (DANN), this study presents a novel deep adaptive adversarial network (DAAN).
The DAAN comprises a condition recognition module and domain adversarial learning module.
The condition recognition module is constructed with a generator to extract features and classify the health condition of machinery automatically.
The domain adversarial learning module is achieved with a discriminator based on Wasserstein distance to learn domain-invariant features.
Then spectral normalization (SN) is employed to accelerate convergence.
The effectiveness of DAAN is demonstrated through three transfer fault diagnosis experiments, and the results show that the DAAN can converge to zero after approximately 15 training epochs, and all the average testing accuracies in each case can achieve over 92%.
It is expected that the proposed DAAN can effectively learn domain-invariant features to bridge the discrepancy between the data from different working conditions.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Jinrui& Ji, Shanshan& Han, Baokun& Bao, Huaiqian& Jiang, Xingxing. 2020. Deep Adaptive Adversarial Network-Based Method for Mechanical Fault Diagnosis under Different Working Conditions. Complexity،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1143440
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Jinrui…[et al.]. Deep Adaptive Adversarial Network-Based Method for Mechanical Fault Diagnosis under Different Working Conditions. Complexity No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1143440
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Jinrui& Ji, Shanshan& Han, Baokun& Bao, Huaiqian& Jiang, Xingxing. Deep Adaptive Adversarial Network-Based Method for Mechanical Fault Diagnosis under Different Working Conditions. Complexity. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1143440
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1143440
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