Abstract

Rolling bearings are important parts of mechanical equipment. However, the early failures of the bearing are usually masked by heavy noise. This brings about difficulties to the extraction of its fault features. Therefore, there is a need to develop a reliable method for early fault detection of the bearing. Considering this issue, a novel fault diagnosis method using the improved wavelet threshold denoising and fast spectral correlation (Fast-SC) is proposed. First, to solve the discontinuity of the hard threshold function and avoid the constant deviation triggered by the soft threshold function, a piecewise continuous threshold function is proposed by using a new threshold selection rule to denoise the original signal. In the new threshold function, the adjuster α is introduced to improve the traditional wavelet denoising algorithm, so as to enhance the signal-to-noise ratio (SNR) of the original signal more effectively. Then, the denoised signal is analysed by Fast-SC to identify the rolling bearing fault features. Finally, simulation analysis and experimental data demonstrate that the proposed approach is effective for rolling bearing fault detection compared with Fast-SC and the combined method based on traditional wavelet threshold and Fast-SC.

Highlights

  • Rolling bearings are widely used in rotating machinery

  • Considering the above problems, this paper presents a novel time-frequency analysis method combining improved wavelet threshold denoising and fast spectral correlation (Fast-spectral correlation (SC)) for rolling bearing fault diagnosis

  • To prove the superiority of this method, the simulated signal is dealt with Fast-SC, the combined method based on traditional wavelet threshold and Fast-SC. e comparison results acquired by the above three methods are shown in Figures 7 to 8

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Summary

Introduction

Rolling bearings are widely used in rotating machinery. they are the most susceptible to be damaged in mechanical systems. Numerous typical signal processing methods are used for fault diagnosis of rolling bearings. Spectral correlation (SC) has played a strong part in the extraction of fault features of rotating machinery. It simultaneously displays the modulations and carriers of a signal in the form of a bispectral map. Considering the above problems, this paper presents a novel time-frequency analysis method combining improved wavelet threshold denoising and Fast-SC for rolling bearing fault diagnosis. En Fast-SC analysis is devoted to the denoised signal to enhance the periodic component of the signal and accurately extract the fault characteristic frequency of the bearing. Compared with the Fast-SC and the combined method using traditional wavelet threshold and Fast-SC, the proposed approach can accurately detect bearing faults.

Algorithmic Flow of the Improved Wavelet Threshold Denoising and Fast-SC
Improved Wavelet Threshold Denoising
Simulation Analysis
Experiment Validation
Conclusions
Full Text
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