Bearing Incipient Fault Detection Method Based on Stochastic Resonance with Triple-Well Potential System
Bearing Incipient Fault Detection Method Based on Stochastic Resonance with Triple-Well Potential System
复制标题
基于三井势系统随机共振的轴承初期故障检测方法
DOI：
10.1007/s12204-020-2238-4
复制
发表时间：
2020-10
期刊：
影响因子：
--
通讯作者：
TAO Qingbao
中科院分区：
文献类型：
--
作者：
LIU Ziwen;XIAO Lei;BAO Jinsong;TAO Qingbao
轴承早期故障特征往往淹没在故障特征微弱的强背景噪声中，使早期故障难以检测。随机共振（SR）被认为是一种有效的方法来检测的初期，但是，输出饱和可能会出现，如果采用随机共振。提出了一种基于三阱电位系统和SR机理的轴承早期故障检测方法。本文采用三势阱势函数的非线性系统，实现了超晶格的高度再现。因此，采用粒子群优化算法对非线性系统中的参数进行优化，优化目标是最大化故障信号的信噪比。优化后得到最优的系统参数，从而产生共振效应，增强轴承的早期故障特性。通过仿真验证和工程应用，验证了该方法的有效性.实验结果表明，该方法能有效地从强背景噪声中检测出初始信号，并能获得比传统方法更好的输出。
Bearing incipient fault characteristics are always submerged in strong background noise with weak fault characteristics, so that the incipient fault is hard to detect. Stochastic resonance (SR) is accepted to be an effective way to detect the incipient; however, output saturation may occur if bistable SR is adopted. In this paper, a bearing incipient fault detection method is proposed based on triple-well potential system and SR mechanism. The achievement of SR highly replays on the nonlinear system which is adopted a triple-well potential function in this paper. Therefore, the parameters in the nonlinear system are optimized by particle swarm optimization algorithm, and the objective of optimization is to maximize the signal-to-noise ratio of the fault signal. After optimization, the optimal system parameters are obtained thereby the resonance effect is generated and the bearing incipient fault characteristic is enhanced. The proposed method is validated by simulation verification and engineering application. The results show that the method is effective to detect an incipient signal from heavy background noise and can obtain better outputs compared with bistable SR.
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DOI：
10.1145/2598394.2605342
发表时间：
2014-07
期刊：
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
影响因子：
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作者：
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通讯作者：
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DOI：
--
发表时间：
2009
期刊：
Journal of Xi'an Jiaotong University
影响因子：
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作者：
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He Zheng-jia
DOI：
10.1016/b978-0-12-409547-2.14581-0
发表时间：
2020
期刊：
Comprehensive Chemometrics
影响因子：
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作者：
Federico Marini;Beata Walczak
通讯作者：
Federico Marini;Beata Walczak
DOI：
10.1201/9781003206477-5
发表时间：
2021-08
期刊：
Evolutionary Optimization Algorithms
影响因子：
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作者：
A. Badar
通讯作者：
A. Badar
DOI：
10.1201/9780429422614-20
发表时间：
2018-10
期刊：
Swarm Intelligence Algorithms
影响因子：
--
作者：
Adam Slowik
通讯作者：
Adam Slowik