Mechanical Fault Feature Extraction under Underdamped Conditions Based on Unsaturated Piecewise Tri-Stable Stochastic Resonance
Source abstract
Abstract
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.
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Research context
OpenAlex topics: stochastic dynamics and bifurcation · Probabilistic and Robust Engineering Design · Force Microscopy Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
School of Information and Artificial Intelligence, Nanchang Institute of Science & Technology, Nanchang 330108, China
Funding
National Natural Science Foundation of China · central government guides local science and technology development fund projects · Nonlinear Dynamics and Application Research Center of Nanchang Institute of Science and Technology
References (32)
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