KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery
المؤلفون المشاركون
Xiao, Zhihuai
Malik, O. P.
Hu, Xiao
Liu, Dong
Tang, Yongjun
Xia, Xiangchen
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-17، 17ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-06-29
دولة النشر
مصر
عدد الصفحات
17
التخصصات الرئيسية
الملخص EN
Feature extraction plays a key role in fault diagnosis of rotating machinery.
Many methods reported in the literature are based on masses of labeled data and need much prior knowledge to select the most discriminating features or establish a complex deep-learning model.
To solve the dilemma, a novel feature extraction method based on kernel principal component analysis (KPCA) and an autoencoder (AE), namely, SFS-KPCA-AE, is presented in this paper to automatically extract the most discriminative features from the frequency spectrum of vibration signals.
First, fast Fourier transform is calculated on the entire vibration signal to get the frequency spectrum.
Next, the spectrum is divided into several segments.
Then, local-global feature extraction is performed by applying KPCA to these segments.
Finally, an AE is employed to obtain the low-dimensional representations of the high-dimensional global feature.
The proposed feature extraction method combined with a classifier achieves fault diagnosis for rotating machinery.
A rotor dataset and a bearing dataset are utilized to validate the performance of the proposed method.
Experimental results demonstrate that the proposed method achieved satisfactory performance in feature extraction when training samples or motor load changed.
By comparing with other methods, the superiority of the proposed SFS-KPCA-AE is verified.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Hu, Xiao& Xiao, Zhihuai& Liu, Dong& Tang, Yongjun& Malik, O. P.& Xia, Xiangchen. 2020. KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1196285
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Hu, Xiao…[et al.]. KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery. Mathematical Problems in Engineering No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1196285
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Hu, Xiao& Xiao, Zhihuai& Liu, Dong& Tang, Yongjun& Malik, O. P.& Xia, Xiangchen. KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1196285
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1196285
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