Preprint Article Version 1 This version is not peer-reviewed

A Periodic Potential Underdamped Stochastic Resonance Method and Its Application for Gear Fault Diagnosis

Version 1 : Received: 26 March 2019 / Approved: 27 March 2019 / Online: 27 March 2019 (08:49:38 CET)

How to cite: Li, Z.; Liu, X.; He, T.; Shan, Y. A Periodic Potential Underdamped Stochastic Resonance Method and Its Application for Gear Fault Diagnosis. Preprints 2019, 2019030250 (doi: 10.20944/preprints201903.0250.v1). Li, Z.; Liu, X.; He, T.; Shan, Y. A Periodic Potential Underdamped Stochastic Resonance Method and Its Application for Gear Fault Diagnosis. Preprints 2019, 2019030250 (doi: 10.20944/preprints201903.0250.v1).

Abstract

The vibration feature of weak gear fault is often covered in strong background noise, which makes it necessary to establish weak feature enhancement methods. Among the enhancement methods, stochastic resonance (SR) has the unique advantage of transferring noise energy to weak signals and has a great application prospection in weak signal extraction. But the traditional SR potential model cannot form a richer potential structure and may lead to system instability when the noise is too great. To overcome these shortcomings, the article presents a periodic potential underdamping stochastic resonance (PPUSR) method after investigating the potential function and system signal-to-noise ratio (SNR). In addition, system parameters are further optimized by using ant colony algorithm. Through simulation and gear experiments, the effectiveness of the proposed method was verified. We concluded that compared with the traditional underdamped stochastic resonance (TUSR) method, the PPUSR method had a higher recognition degree and better frequency response capability.

Subject Areas

fault diagnosis, stochastic resonance, periodic potential, underdamped, weak signal

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