Fault Characteristic Extraction by Fractional Lower-Order Bispectrum Methods
المؤلفون المشاركون
You, Fang
Wang, Haibin
Liu, Zeliang
Long, Junbo
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-24، 24ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-12-31
دولة النشر
مصر
عدد الصفحات
24
التخصصات الرئيسية
الملخص EN
The generated signals generally contain a large amount of background noise when the mechanical bearing fails, and the fault signals present nonlinear and non-Gaussian feature, which have heavy tail and belong to α-stable distribution (1<α<2); even the background noises are also α-stable distribution process.
Then it is difficult to obtain reliable conclusion by using the traditional bispectral analysis method under α-stable distribution environment.
Two improved bispectrum methods are proposed based on fractional lower-order covariation in this paper, including fractional low-order direct bispectrum (FLODB) method, fractional low-order indirect bispectrum (FLOIDB) method.
In order to decrease the estimate variance and increase the bispectral flatness, the fractional lower-order autoregression (FLOAR) model bispectrum and fractional lower-order autoregressive moving average (FLOARMA) model bispectrum methods are presented, and their calculation steps are summarized.
We compare the improved bispectrum methods with the conventional methods employing second-order statistics in Gaussian and SαS distribution environments; the simulation results show that the improved bispectrum methods have performance advantages compared to the traditional methods.
Finally, we use the improved methods to estimate the bispectrum of the normal and outer race fault signal; the result indicates that they are feasible and effective for fault diagnosis.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Haibin& Long, Junbo& Liu, Zeliang& You, Fang. 2020. Fault Characteristic Extraction by Fractional Lower-Order Bispectrum Methods. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-24.
https://search.emarefa.net/detail/BIM-1201610
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Haibin…[et al.]. Fault Characteristic Extraction by Fractional Lower-Order Bispectrum Methods. Mathematical Problems in Engineering No. 2020 (2020), pp.1-24.
https://search.emarefa.net/detail/BIM-1201610
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Haibin& Long, Junbo& Liu, Zeliang& You, Fang. Fault Characteristic Extraction by Fractional Lower-Order Bispectrum Methods. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-24.
https://search.emarefa.net/detail/BIM-1201610
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1201610
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