Generalized stepwise demodulation transform and synchrosqueezing for time–frequency analysis and bearing fault diagnosis

2016 ◽  
Vol 368 ◽  
pp. 202-222 ◽  
Author(s):  
Juanjuan Shi ◽  
Ming Liang ◽  
Dan-Sorin Necsulescu ◽  
Yunpeng Guan
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Rolling Bearings Fault Diagnosis Based on Generalized Demodulation Time-Frequency Analysis Method

2014 ◽  
Vol 971-973 ◽  
pp. 701-704
Author(s):  
Yong Xia Bu ◽  
Jian De Wu ◽  
Jun Ma ◽  
Yu Gang Fan ◽  
Xiao Dong Wang
Keyword(s):  
Fault Diagnosis ◽  
Energy Spectrum ◽  
Frequency Analysis ◽  
Rolling Element Bearing ◽  
Original Signal ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Bearing Fault ◽  
Instantaneous Energy

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.

scholarly journals Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Bo Wu ◽  
Zhe Tong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Optimal Number ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

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