Abstract
In this paper a stochastic resonance (SR)-based method for recovering weak impulsive signals is developed for quantitative diagnosis of faults in rotating machinery. It was shown in theory that weak impulsive signals follow the mechanism of SR, but the SR produces a nonlinear distortion of the shape of the impulsive signal. To eliminate the distortion a moving least squares fitting method is introduced to reconstruct the signal from the output of the SR process. This proposed method is verified by comparing its detection results with that of a morphological filter based on both simulated and experimental signals. The experimental results show that the background noise is suppressed effectively and the key features of impulsive signals are reconstructed with a good degree of accuracy, which leads to an accurate diagnosis of faults in roller bearings in a run-to failure test.
Highlights
An impulsive signal is a typical vibration response due to faults in many mechanical components such as bearings and gears
The experimental results show that the background noise is suppressed effectively and the key features of impulsive signals are reconstructed with good degree of accuracy, which leading to an accurate diagnosis of faults on bearings undertaking a run-to failure test
A stochastic resonance (SR)-based method of recovering weak impulsive signals is developed for quantitative diagnosis of faults in rotating machinery
Summary
An impulsive signal is a typical vibration response due to faults in many mechanical components such as bearings and gears. Yu et al [9] applied the EMD and Hilbert method to extract the envelope signal of rolling bearings and found that the fault characteristics can be extracted by selecting proper IMFs. A morphological filter is an efficient tool in processing impulsive signals. Laplace wavelet correlation filtering (LWCF), which uses a Laplace wavelet as the transient model and identifies the parameters by correlation filtering, is effective in detecting a single transient [10] These methods are not suitable for extracting signal features of impulsive signals with strong background noise. Using the fault period and phase obtained in the first SR, we can intercept the data that contain only single shocks of attenuation signals. The experimental results show that the background noise is suppressed effectively and the key features of impulsive signals are reconstructed with good degree of accuracy, which leading to an accurate diagnosis of faults on bearings undertaking a run-to failure test
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