دورية أكاديمية
أعرض في EDS

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

التفاصيل البيبلوغرافية
العنوان: Mechanical Fault Feature Extraction under Underdamped Conditions Based on Unsaturated Piecewise Tri-Stable Stochastic Resonance
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
الوصف
تدمد:20763417
DOI:10.3390/app13020908