Chaotic resonance in a fractional-order oscillator system with application to mechanical fault diagnosis. (January 2021)

• Record Type: Journal Article
• Authors: He, Yuzhu; Fu, Yuxuan; Qiao, Zijian; Kang, Yanmei
• Is Part Of: Chaos, solitons and fractals. Volume 142(2021)
• Publication Date: 2021-01
• Subjects: Chaotic resonance -- Fractional-order Duffing oscillator -- Mechanical fault diagnosis -- Weak signal detection; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2020.110536
• Languages: English
• ISSNs: 0960-0779

Abstract

Highlights: The dynamic characteristics of fractional-order Duffing system is investigated and the chaotic interval of the system is evaluated by means of bifurcation diagram and the maximal Lyapunov exponent It is disclosed that chaotic resonance can occur in the model, with the optimal parameter for chaotic resonance falls into the chaotic interval. That is, the weak signal can be optimally amplified by the intrinsic fluctuations in chaotic system instead of stochastic noise. Based on the model research, a novel weak signal detection method is proposed and successfully applied to mechanical fault diagnosis without signal preprocessing, and the numerical result shows that the signal-to-noise ratio of the fault characteristic can be greatly improved. Abstract: This paper is to generalize the research on chaotic resonance (CR) towards fractional-order chaotic systems and then develop a new technique for detecting weak signals embedded in strong background noise. For illustration, a fractional-order Duffing oscillator system is evaluated by means of bifurcation analysis, revealing the phenomenon of chaotic resonance, with the optimal driving amplitude falling within a chaotic interval. It is found that the weak signal can be amplified by the intrinsic fluctuations in the chaotic system instead of stochastic noise. Based on this investigation, a novel weak signal detection method is developed and successfully applied to mechanical fault diagnosis without the need of signal preprocessing. Extensive numerical results show that the signal-to-noise ratio of the incipient fault signal of machinery can be greatly improved.