Abstract
The stochastic resonance (SR) method is widely applied to fault feature extraction of rotary machines, which is capable of improving the weak fault detection performance by energy transformation through the potential well function. The potential well functions are mostly set fixed to reduce computational complexity, and the SR methods with fixed potential well parameters have better performances in stable working conditions. When the fault frequency changes in variable working conditions, the signal processing effect becomes different with fixed parameters, leading to errors in fault detection. In this paper, an underdamped second-order adaptive general variable-scale stochastic resonance (USAGVSR) method with potential well parameters’ optimization is put forward. For input signals with different fault frequencies, the potential well parameters related to the barrier height are figured out and optimized through the ant colony algorithm. On this basis, further optimization is carried out on undamped factor and step size for better fault detection performance. Cases with diverse fault types and in different working conditions are studied, and the performance of the proposed method is validated through experiments. The results testify that this method has better performances of weak fault feature extraction and can accurately identify different fault types in the input signals. The method proves to be effective in the weak fault extraction and classification and has a good application prospect in rolling bearings’ fault feature recognition.
Highlights
Rotary machines are generally applied to modern industrial production, and rolling bearings play a key role in rotary machines [1,2,3]
Optimizing the barrier height is the basis for the system to achieve the optimal output. erefore, an underdamped second-order adaptive general variable-scale stochastic resonance method with the optimization of potential well parameters is introduced for bearing fault detection
If the cutoff condition is satisfied, that is, when a certain number of iterations are reached, signal-to-noise ratio (SNR) tends to be stable, the optimal barriers corresponding to the vibration signals are obtained, and the optimal parameters a1 and b1 are output. en, the vibration signals are input to the underdamped second-order stochastic resonance system and take advantage of the corresponding most advantageous barriers, and the system parameters are initialized. e optimum SNR is searched by the optimization algorithm, and the optimal output parameters are obtained. e corresponding frequency domain graphs of vibration signals are obtained, the weak fault features are extracted, and the fault types are identified
Summary
Rotary machines are generally applied to modern industrial production, and rolling bearings play a key role in rotary machines [1,2,3]. Ese methods can extract the characteristics of weak signals by eliminating or suppressing noise In this process, the weak fault information of rolling bearings is possible to be suppressed. Li and Shi [24] introduced a new piecewise nonlinear SR to enhance early fault features of machinery He et al [25] analyzed the multiscale SR spectral method, which studied the time-frequency distribution of the signal and used its scale as a modulation system to recognize the fault characteristics of rolling bearings. Erefore, an underdamped second-order adaptive general variable-scale stochastic resonance method with the optimization of potential well parameters is introduced for bearing fault detection. On the basis of the most dominant barrier, the optimal matching of noise, the input signal, and the nonlinear system are realized, and the weak fault features of bearings are recognized. The rolling bearing faults under different working conditions are studied, and the accuracy of the proposed method is verified. e sixth part is the result and discussion of this paper. e seventh part draws the conclusion of this paper
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