An Improved Multiscale Stochastic Resonance Method for Bearing Fault Diagnosis

被引：0
|
作者：
Li, Zhiyuan [1 ]
Lu, Siliang [1 ,2 ]
Wang, Haoxuan [1 ]
机构：
[1] Anhui Univ, Coll Elect Engn & Automat, Hefei 230601, Peoples R China
[2] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China
来源：
PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020) | 2020年
基金：
中国国家自然科学基金;
关键词：
Bearing Fault diagnosis; Autocorrelation Function; Multiscale Noise Tuning Stochastic Resonance;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Stochastic resonance (SR) has been widely used for bearing fault diagnosis. The multiscale noise tuning stochastic resonance (MSTSR) has been proven effective to analyze the weak bearing fault signals. However, the effect of MSTSR is not very satisfactory in practical application. The autocorrelation function (ACF) of original signals can extract the periodic components effectively. To enhance the weak signal enhancement performance, this paper proposes a method that combines the ACF and MSTSR. By applying ACF method in the system before MSTSR, a high output signal to noise ratio (SNR) can be obtained. The performance of the proposed method has been validated by comparing with the effect of MSTSR method on different types of bearing fault signals.
引用
收藏
页码：5107 / 5111
页数：5
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