A novel stochastic resonance model based on bistable stochastic pooling network and its application. (April 2021)

• Record Type: Journal Article
• Authors: Zhang, Wenyue; Shi, Peiming; Li, Mengdi; Han, Dongying
• Is Part Of: Chaos, solitons and fractals. Volume 145(2021)
• Publication Date: 2021-04
• Subjects: Stochastic resonance -- Bistable stochastic pooling network -- Noise-induced -- SNR; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2021.110800
• Languages: English
• ISSNs: 0960-0779

Abstract

Highlights: A novel stochastic resonance model based on bistable stochastic pooling network is proposed. The least mean square algorithm is used to optimize the BSPN output vector optimized by random noise with linear weighting. The SNR of the output signal is deduced, and the change of SNR with the number of network nodes is discussed. Some examples are used to verify the weak signal detection capability of the BSPN model. Abstract: Analysing the vibration and sound signals of machine components is the primary approach for machine condition monitoring and fault diagnosis. However, due to the special working operating conditions of rotating machinery, the collected signals often contain strong noise components generated by other parts of the machine and harsh environment. These noises severely affect the analysis and processing of the target signal. Stochastic resonance (SR) is an effective technique to extract and enhance periodic or aperiodic signals submerged in noise. Consequently, SR has been widely used for fault diagnosis of rotating machinery. In this study, a bistable stochastic pooling network (BSPN) model based on the traditional SR model is proposed to improve the efficiency of weak fault diagnosis. The least mean square algorithm is used to perform linear weighted optimization on the output vector of random noise-optimized BSPN. At the same time, the optimal weight vector of the random stochastic pooling networks with any number of nodes is obtained. Subsequently, analog signals are used to examine the output signal-to-noise ratio (SNR) of the BSPN. Finally, the efficacy of BSPN system is validated through bearing data collected by two different experimental systems. The experimental results indicate that ordinary array system cannot avoid frequency conversion interference, so it is unable to extract extremely weak fault signals. On the contrary, the BSPN system can accurately detect the weak.