Abstract
In engineering practice, the bearing fault signal is composed of a series of complex multi-component signals containing multiple fault characteristics information. In the early stage of fault sprouting and evolution, the fault features are easily disturbed by noise and irrelevant signals, eliminating the fault signals in the strong background noise. To overcome the influence of noise on the signal, this study proposes multi-frequency weak signal decomposition and reconstruction of rolling bearing based on adaptive cascaded stochastic resonance. First, the original signal is passed through the Hilbert transform to obtain the envelope signal. The envelope signal is high-pass filtered to eliminate the interference of low-frequency components on the response of the stochastic resonance system. Secondly, cascaded stochastic resonance system parameters are adaptively optimized by the quantum particle swarm algorithm (QPSO). The high-pass filtered signal input to the adaptive cascaded stochastic resonance system (ACSRS) can further enhance the weak fault characteristics, allowing the gradual transfer of high-frequency noise energy to the low-frequency fault characteristic components. Finally, the signal is decomposed using the variational mode decomposition (VMD) method to jointly determine the location of the fault characteristic frequencies in the intrinsic mode functions (IMF) component by the energy loss coefficient and correlation coefficient to achieve the reconstruction of multi-frequency weak signals. Through simulation and experimental validation, the effectiveness and superiority of the method for multi-frequency weak signal detection in bearings are verified. The results show that the method not only achieves the adaptive optimization of the stochastic resonance system parameters gradually removing the high-frequency noise in the signal and improving the energy of the low-frequency signal but also reduces the number of decomposition layers of the VMD, enhances the fault characteristic information in the weak signal, and effectively identifies the early weak fault characteristics of rolling bearings.
Highlights
Rolling bearings, as the most important supporting parts of rotating machinery are called “industrial joints”; the application of rolling bearings covers almost all industrial fields
The method can reduce the number of variational mode decomposition (VMD) decomposition layers while adaptively seeking the optimization of the parameters of the stochastic resonance system, so that the energy of the high-frequency noise is transferred to the low-frequency fault characteristic component, enhancing the fault characteristic information in the weak signal
A rolling bearing multi-frequency weak signal decomposition and reconstruction method based on the adaptive cascaded stochastic resonance is proposed
Summary
As the most important supporting parts of rotating machinery are called “industrial joints”; the application of rolling bearings covers almost all industrial fields. Lai et al [30] proposed an adaptive multi-parameter tuned stochastic resonance method for bistable systems based on particle swarm optimization algorithm, which generates optimal SR output by adaptively adjusting multiple parameters to achieve fault feature extraction and further fault diagnosis of rolling bearings. To achieve the detection of rolling bearing weak signals under strong background noise, combining the respective advantages of noise reduction and signal enhancement methods, this paper proposes an adaptive cascaded stochastic resonance method for decomposition and reconstruction of rolling bearing multi-frequency weak signals. The method can reduce the number of VMD decomposition layers while adaptively seeking the optimization of the parameters of the stochastic resonance system, so that the energy of the high-frequency noise is transferred to the low-frequency fault characteristic component, enhancing the fault characteristic information in the weak signal.
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