Inaccessible rolling bearing diagnosis using a novel criterion for Morlet wavelet optimization

2021 ◽  
pp. 107754632198950
Author(s):  
Mehdi Behzad ◽  
Amirmasoud Kiakojouri ◽  
Hesam Addin Arghand ◽  
Ali Davoodabadi

The objective of this research is to diagnose an inaccessible rolling bearing by indirect vibration measurement. In this study, a shaft supported with several bearings is considered. It is assumed that the vibration for at least one bearing is not recordable. The purpose is to diagnose inaccessible bearing by the recorded data from the sensors located on the other bearings. To achieve this goal, the continuous wavelet transform is used to detect weak signatures in the available vibration signals. A new criterion for adjusting the scale parameter of continuous wavelet transform is proposed based on the amplitude of the bearing characteristic frequencies. In this criterion, the optimal scale is selected to maximize the amplitude of bearing characteristic frequencies in comparison with the amplitude of the other frequencies. The results of the proposed method are compared with a popular method, energy-to-entropy ratio criterion, using two different sets of run-to-failure experimental data. Results indicate that the proposed method in this article is more effective and efficient for extracting the weak signatures and diagnosing inaccessible bearings from the recorded vibration signals.

Author(s):  
R. S. Pathak ◽  
S. K. Singh

The continuous wavelet transform is studied on certain Gel'fand–Shilov spaces of type S. It is shown that, for wavelets belonging to the one type of S-space defined on R, the wavelet transform is a continuous linear map of the other type of the S-space into a space of the same type (latter type) defined on R × R+. The wavelet transforms of certain ultradifferentiable functions are also investigated.


2006 ◽  
Vol 321-323 ◽  
pp. 1233-1236
Author(s):  
Sang Kwon Lee ◽  
Jang Sun Sim

Impulsive sound and vibration signals in gear system are often associated with their faults. Thus these impulsive sound and vibration signals can be used as indicators in condition monitoring of gear system. The traditional continuous wavelet transform has been used for detection of impulsive signals. However, it is often difficult for the continuous wavelet transform to identify spikes at high frequency and meshing frequencies at low frequency simultaneously since the continuous wavelet transform is to apply the linear scaling (a-dilation) to the mother wavelet. In this paper, the spike wavelet transform is developed to extract these impulsive sound and vibration signals. Since the spike wavelet transform is to apply the non-linear scaling, it has better time resolution at high frequency and frequency resolution at low frequency than that of the continuous wavelet transform respectively. The spike wavelet transform can be, therefore, used to detect fault position clearly without the loss of information for the damage of a gear system. The spike wavelet transform is successfully is applied to detection of the gear fault with tip breakage.


2009 ◽  
Vol 413-414 ◽  
pp. 651-657 ◽  
Author(s):  
Ru Jiang Hao ◽  
Zhi Peng Feng ◽  
Fu Lei Chu

The acoustic emission signals of rolling bearing with different type of defects are de-noised and illustrated by the continuous wavelet transform and scalogram. Morlet wavelet function is selected and the wavelet parameters are optimized based on the principle of minimal wavelet entropy. The soft-threshold de-noising is used to filter the wavelet transform coefficients. The de-noised signals obtained by reconstructing the wavelet coefficients show the obvious impulsive features. Based on the optimized waveform parameters, the wavelet scalogram is used to analyze the real AE signal from the defective rolling bearing in experimental test rig. The results indicate that the proposed method is useful and efficient for signal purification and features extraction.


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