scholarly journals Time-frequency analysis method of bearing fault diagnosis based on the generalized S transformation

2017 ◽  
Vol 19 (6) ◽  
pp. 4221-4230 ◽  
Author(s):  
Jianhua Cai ◽  
Yongliang Xiao
2014 ◽  
Vol 971-973 ◽  
pp. 701-704
Author(s):  
Yong Xia Bu ◽  
Jian De Wu ◽  
Jun Ma ◽  
Yu Gang Fan ◽  
Xiao Dong Wang

In view of the characteristics of the non-stationary and multi-component AM-FM signals of vibration signals in the rolling element bearing, the generalized demodulation time-frequency analysis method is used for its fault diagnosis, overcoming the problem that the maximal overlap discrete wavelet packet transform (MODWPT) has no adaptability. First of all, the original vibration signal is took preprocessing by generalized Fourier; Then, using MODWPT to decompose signals after pretreatment and obtaining weights; Once again, the weights are carried out the inverse generalized Fourier transform to get the weights of the original signal; Finally, reconstructing principal component of the original signal to get the Hilbert instantaneous energy spectrum, roller bearing fault diagnoses based on the Hilbert instantaneous energy spectrum. The experimental results show that the method can effectively diagnose rolling bearing fault.


Author(s):  
Yue Hu ◽  
Xiaotong Tu ◽  
Fucai Li

The planetary gearbox is one of the key components in the rotating machinery. The planetary gearbox is prone to malfunction, which increases downtime and repair costs. Hence, the fault diagnosis of the planetary gearbox is an important research topic. The acquired signal from the planetary gearbox exhibit strongly time-variant and nonstationary features since the planetary gearbox usually works at time-varying speeds. In this study, a new time-frequency analysis method is proposed. This method takes the spectrum shape into account and partitions the time-frequency into several components. Then the fault feature of the planetary gearbox is detected by analyzing the decomposed components. The simulated signal and the experimental signals under nonstationary conditions are analyzed to verify the effectiveness the proposed method. Results show that the proposed method can efficiently extract the fault feature of the planet gear.


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