Abstract
Based on traditional theory of bistable stochastic resonance, this paper puts forward a piecewise stochastic resonance model which used to detect medium- low-frequency weak periodic signal and deducts analytical results of signal-to-noise ratio. Besides, we have done simulation experiments and contrast analysis of the two models also. The result shows that the piecewise model is better to detect medium- low-frequency weak periodic signal in background of strong noise with higher SNR and wider frequency range. It has raised adaptability to detect periodic signal which is common in mechanical failure. Combined with the piecewise bistable stochastic resonance model and the modulate method, it is more effectively to achieve judgment and detection of the mechanical fault signals.
Highlights
Human beings have already developed a series of test methods and techniques in weak signal detection, such as correlation filtering technology, etc,but they all need to restrain noise
In engineering practice such as mechanical failure, measured signal is medium-low-frequency periodic signal which is often overwhelmed by noise and this signal is much weaker than noise
Compared with output signal-to-noise ratio (SNR) of the two models, the piecewise model can meet the needs of the wider frequency range and strong noise effectively because it doesn't appear saturation
Summary
Human beings have already developed a series of test methods and techniques in weak signal detection, such as correlation filtering technology, etc,but they all need to restrain noise. The stochastic resonance has provided a useful method to extract weak signal in background of strong noise. A lot of research represented by adiabatic approximation theory has shown that merely in small parameters condition (the signal amplitude, frequency and noise intensity are much less than 1), the system can produce stochastic resonance to detect the weak signal effectively[1,2,3].
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