Abstract
Extraction and enhancement of weak impulse signature is the key of rolling bearing fault prognostics in which case the features are often weak and covered by noise. Tunable Q-factor wavelet transform (TQWT), as an emerging wavelet construction theory developed in a frequency domain explicitly, has the advantages of matching with the specific oscillation behavior of signal components. In this article, an adaptive sparse representation (ASR) method is proposed, which integrates the sparse code shrinkage (SCS) and parameter optimization into TQWT. However, direct application of ASR is difficult to extract fault signatures at the early stage or low-speed operation due to weak fault symptoms and background noise. A novel fault diagnosis strategy based on continuous wavelet transform (CWT) and ASR is investigated. CWT owns significant advantages on multiscale subdivision and weak signal detection. The results of simulated and experimental vibration signal analyses verify the effectiveness of the proposed method in accurately extracting weak impulse features from the noise environment.
Highlights
Rolling bearings are key components in the mechanical industry, such as wind turbine, warship, aeroengine, and high-end machine tools
Such premature failures in the bearing systems are always subjected to losses in time and finance, or even sometimes, they may lead to catastrophic consequences
For the optimal scale selection of continuous wavelet transform (CWT), we propose the strategy of weighted Shannon entropy. e main contributions of this paper are in two categories
Summary
Rolling bearings are key components in the mechanical industry, such as wind turbine, warship, aeroengine, and high-end machine tools. Localized defects are often initiated by insufficient lubrication film between the surfaces that are in contact or normal fatigue failure, and the defects grow in size and change shape over time It is one of the most reliable and sensitive technical means for bearing diagnosis to extract fault features through vibration response collected by an acceleration sensor. A new adaptive sparse representation (ASR) method is proposed, which can well match the damped oscillation mode of the bearing fault signal and enhance the transient impulse characteristics. E rolling bearing fault signal has the remarkable property of periodic transient impulse, and the Morlet wavelet function is very similar to the impulse signal, so it is comparatively suitable for selecting it to extract an impulse feature. The kurtosis value K is sensitive to the outliers or impulses generated by mechanical fault because it is proportional to the fourth-order higher moment. erefore, considering both Shannon entropy and kurtosis would provide a more reliable scale selection process. e weighted Shannon entropy is defined as EK E. (5) K en, the best scale can be found by the minimum value of EK. e scale selected by EK is considered to contain the signal nature closest to the impulses produced by the mechanical fault. is selection method can reduce the side effects of noise and unrelated signals from periodic harmonic components
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