Fault diagnosis method and application based on unsaturated piecewise linear stochastic resonance
Zhixing Li1, Xiandong Liu1, Songjiu Han2
1School of Transportation Science and Engineering, Beihang University, Beijing, China.
The Review of Scientific Instruments
|July 1, 2019
Summary
A new unsaturated piecewise linear stochastic resonance (PLSR) method effectively detects weak mechanical fault signals. This approach overcomes classical bistable stochastic resonance saturation, improving signal extraction in noisy environments for bearing diagnostics.
Area of Science:
- Mechanical Engineering
- Signal Processing
- Nonlinear Dynamics
Background:
- Classical bistable stochastic resonance (CBSR) is used for mechanical fault diagnosis but suffers from output saturation, hindering weak signal extraction from noise.
- Saturation in CBSR limits the amplitude of output signals, making it difficult to detect subtle mechanical faults masked by significant background noise.
Purpose of the Study:
- To introduce and analyze an unsaturated piecewise linear stochastic resonance (PLSR) mechanism for improved weak signal detection.
- To develop a novel method for bearing fault diagnosis that effectively extracts weak signals in high-noise conditions.
Main Methods:
- Developed a piecewise linear potential model allowing independent adjustment of barrier height and potential wall inclination.
- Derived the output signal-to-noise ratio (SNR) equation for the PLSR system.
- Analyzed the influence of signal strength, potential parameters, and angular frequency on SNR.
Main Results:
- The PLSR model demonstrates an unsaturated characteristic, ensuring output signal amplitude increases with input signal amplitude.
- Optimal SNR is achievable by adjusting the potential parameters in the PLSR system.
- Simulated and experimental results confirm PLSR's superiority over CBSR for weak signal detection in bearing fault diagnosis.
Conclusions:
- The proposed PLSR method effectively extracts weak fault signals from rolling bearings amidst strong background noise.
- PLSR offers a significant advancement over CBSR for mechanical fault diagnosis, particularly in challenging noisy environments.
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