دورية أكاديمية
Mechanical Fault Feature Extraction under Underdamped Conditions Based on Unsaturated Piecewise Tri-Stable Stochastic Resonance
العنوان: | Mechanical Fault Feature Extraction under Underdamped Conditions Based on Unsaturated Piecewise Tri-Stable Stochastic Resonance |
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المؤلفون: | Shuai Zhao, Peiming Shi |
المصدر: | Applied Sciences, Vol 13, Iss 2, p 908 (2023) |
بيانات النشر: | MDPI AG, 2023. |
سنة النشر: | 2023 |
المجموعة: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
مصطلحات موضوعية: | stochastic resonance, unsaturated system, underdamped conditions, feature extraction, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
الوصف: | 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. |
نوع الوثيقة: | article |
وصف الملف: | electronic resource |
اللغة: | English |
تدمد: | 2076-3417 |
Relation: | https://www.mdpi.com/2076-3417/13/2/908; https://doaj.org/toc/2076-3417 |
DOI: | 10.3390/app13020908 |
URL الوصول: | https://doaj.org/article/03582cbf1e9540ee873170aaede7b3e2 |
رقم الأكسشن: | edsdoj.03582cbf1e9540ee873170aaede7b3e2 |
قاعدة البيانات: | Directory of Open Access Journals |
تدمد: | 20763417 |
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DOI: | 10.3390/app13020908 |