Mechanical Fault Feature Extraction under Underdamped Conditions Based on Unsaturated Piecewise Tri-Stable Stochastic Resonance

被引:5
|
作者
Zhao, Shuai [1 ,2 ]
Shi, Peiming [1 ]
机构
[1] Yanshan Univ, Sch Elect Engn, Qinhuangdao 066004, Peoples R China
[2] Nanchang Inst Sci & Technol, Sch Informat & Artificial Intelligence, Nanchang 330108, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 02期
基金
中国国家自然科学基金;
关键词
stochastic resonance; unsaturated system; underdamped conditions; feature extraction; SYSTEM;
D O I
10.3390/app13020908
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
In the case of the rapid development of large machinery, the research of mechanical fault signal feature extraction is of great significance, it can not only ensure the development of the economy but also ensure safety. Stochastic resonance (SR) is of widespread use in feature extraction of mechanical fault signals due to its excellent signal extraction capability. Compared with an overdamped state, SR in an underdamped state is equivalent to one more filtering of the signal, so the signal-to-noise ratio (SNR) of the output signal will be further improved. In this article, based on the piecewise tri-stable SR (PTSR) obtained from previous studies, the feature extraction of mechanical fault signals is carried out under underdamped conditions, and it is found that the SNR of the output signal is further improved. The simulation signals and experimental signals are used to verify that PTSR has better output performance under underdamped conditions.
引用
收藏
页数:11
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