2. Piecewise Nonlinear Asymmetric Bistable Stochastic Resonance Model
2.1. The Saturation Phenomena of CBSR Model
2.2. PNABSR Model and Its Performances
2.3. Optimization of PNABSR Parameters
3. Analysis and Discussion
3.1. Fault Detection of Bearing SKF 6205-2RS
- Bearing 6205-2RS JEM SKF with Weak Inner Ring Fault
- Bearing 6205-2RS JEM SKF with Weak Outer Ring Fault
3.2. Weak Fault Detection Experiments of Bearing 6200-NR NSK
- 6200-NR NSK Bearing with Weak Inner Ring Fault
- 6200-NR NSK Bearing with Weak Outer Ring Fault
- In this paper, a coupled piecewise nonlinear asymmetric bistable stochastic resonance system is proposed, and the signal-to-noise ratio equation is derived.
- Using the ant colony intelligent algorithm to optimize parameters a and b, the signal-to-noise ratio of the PNABSR model can be 45% higher than that of the traditional CBSR model. It is easier for the PNABSR model to induce stochastic resonance.
- The test results show that the PNABSR model can observe the fault feature more clearly in practical applications. By comparing the experimental results of the CBSR and PNABSR models, it was found that the PNABSR system has outstanding advantages in terms of the amplitude amplification of weak fault characteristics and in improving the signal-to-noise ratio.
- The PNABSR model is suitable for weak fault extraction, especially under the conditions of strong background noise. Because the effectiveness of this model depends on parameter optimization, there is a certain time delay for real-time fault diagnosis.
- The extraction of the weak fault features is the first step of fault diagnosis. However, fault classification, fault degree evaluation, and prediction are also very important for the operation and maintenance of engineering equipment. We intend to discuss these problems in our future work.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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