摘要
Full-period signal acquisition of vibration signal plays a vital role in the health monitoring and fault diagnosis of modern industrial equipment group. The traditional full-period signal acquisition methods usually need not only a reference signal generated from special key phase device but also a reserved position, which is only suitable for a small number of particular equipment. A novel full-period signal acquisition method without key phase is proposed to construct the time-frequency method with strong energy concentration called the synchrosqueezing generalized S-Transform(SGST), combining together the Teager energy operator(TEO) and self-adaptive correlation analysis(SACA) based on the vibration signals of both gear and cylinder head. Actual vibration signals of diesel engine are employed to verify the feasibility and effectiveness of the proposed method by comparing with traditional method with special key phase device. By comparisons, the results show that full-period signal acquisition method without key phase has approximate accuracy for diesel engine under different working conditions.
作者
Zhao Haipeng
Mao Zhiwei
Zhang Jinjie
Lai Yuehua
Wang Zijia
Jiang Zhinong
赵海朋;Mao Zhiwei;Zhang Jinjie;Lai Yuehua;Wang Zijia;Jiang Zhinong(Key Laboratory of Engine Health Monitoring-Control and Networking of Ministry of Education,Beijing University of Chemical Technology,Beijing 100029,P.R.China;Beijing Key Laboratory of High-End Mechanical Equipment Health Monitoring and Self-Recovery,Beijing University of Chemical Technology,Beijing 100029,P.R.China)
基金
Supported by the National Key Research and Development Plan of China(No.2016YFF0203305)
the Fundamental Research Funds for the Central Universities of China(No.JD1912)
Double First-Rate Construction Special Funds(No.ZD1601).
作者简介
赵海朋,born in 1989.He is currently working toward his Ph.D degree in Key Laboratory of Engine Health Monitoring-Control and Networking of Ministry of Education,Beijing University of Chemical Technology.His research interests include the condition monitoring,signal process,intelligent diagnosis,machine learning and deep learning;Zhang Jinjie,To whom correspondence should be addressed.E-mail:zjj87427@163.com。
