Research on Fault Feature Extraction Method Based on FDM-RobustICA and MOMEDA
المؤلفون المشاركون
Wang, Xiaodong
Li, Xuefeng
Wu, Limei
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-23، 23ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-06-22
دولة النشر
مصر
عدد الصفحات
23
التخصصات الرئيسية
الملخص EN
Aiming at the difficulty of extracting rolling bearing fault features under strong background noise conditions, a method based on the Fourier decomposition method (FDM), robust independent component analysis (RobustICA), and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed.
Firstly, the FDM method is introduced to decompose the single-channel bearing fault signal into several Fourier intrinsic band functions (FIBF).
Secondly, by setting the cross-correlation coefficient and kurtosis as a new selection criterion, it can effectively construct the virtual noise channel and the observation signal channel, which makes RobustICA complete the separation of the useful signal and noise well.
Finally, MOMEDA is introduced to enhance the periodic impact components in the denoised signal, and then the filtered signal is analyzed by the Hilbert envelope spectrum to extract the fault characteristic frequency.
Through the experimental analysis of the simulated signals and the actual bearing fault signals, the results show that the proposed method not only has the ability to suppress noise and accurately extract fault feature information but also has better performance than the traditional method of local mean decomposition (LMD) and intrinsic time-scale decomposition (ITD), highlighting its practicality in the fault diagnosis of rotating machinery.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Xiaodong& Li, Xuefeng& Wu, Limei. 2020. Research on Fault Feature Extraction Method Based on FDM-RobustICA and MOMEDA. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-23.
https://search.emarefa.net/detail/BIM-1197262
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Xiaodong…[et al.]. Research on Fault Feature Extraction Method Based on FDM-RobustICA and MOMEDA. Mathematical Problems in Engineering No. 2020 (2020), pp.1-23.
https://search.emarefa.net/detail/BIM-1197262
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Xiaodong& Li, Xuefeng& Wu, Limei. Research on Fault Feature Extraction Method Based on FDM-RobustICA and MOMEDA. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-23.
https://search.emarefa.net/detail/BIM-1197262
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1197262
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