Abstract
Bearing fault diagnosis is vital to guarantee the safe operation of rotating machines. Due to the enhancement principle of energy conversion from noise to weak signal, noise-assisted stochastic resonance (SR) methods have been widely applied. In this paper, to utilize the memory-dependent property of the mechanical degradation process, we develop a scale-transformed fractional oscillator (SFO) driven by a unilateral attenuated impulse signal, and reveal the active effect of generalized SR (GSR) on the energy conversion from internal multiplicative noise to signal. By applying the quantum particle swarm optimization algorithm in the multi-parameter regulation, we propose the adaptive GSR-SFO diagnosis method to realize the enhancement of weak fault characteristics. The experimental results demonstrate that the proposed method is valid and exhibits superiority in diagnosis performance, especially in several typical difficult cases, such as smeared bearing fault caused by mechanical looseness, smeared bearing fault disturbed by strong random pulses, and corrupted bearing fault disturbed by patches of electrical noise.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.