Open AccessImproved SNR to detect the unknown characteristic frequency by SR
Author(s) -
Zhang Jingling,
Yang Jianhua,
Liu Houguang,
Zhou Dengji
Publication year - 2018
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2018.0046
Subject(s) - physics , statistics , computer science , mathematics , pattern recognition (psychology) , artificial intelligence
Stochastic resonance (SR) is widely used in signal processing issues. The classic evaluation index of SR must know the characteristic frequency in prior. However, the accuracy frequency which needs to be detected is not known in advance. To solve this problem, the authors propose a new index, which calls improved signal‐to‐noise ratio (SNR) in adaptive SR. This new index is effective without knowing the accuracy characteristic frequency first. Meanwhile, the general scale transformation and random particle swarm optimisation algorithm are used to satisfy the conditions of SR and help to obtain the optimal system parameters. On the basis of this new index, the simulation and experimental bearing fault signals are both processed perfectly when compared with the classic SNR index. More importantly, it overcomes the drawbacks of the classic SNR index that the accuracy characteristic frequency must be known in advance. Therefore, these results indicate that new index has important practical values in signal processing issues.