Abstract
Vibration signals are used to diagnosis faults of the rolling bearing which is symmetric structure. Stochastic resonance (SR) has been widely applied in weak signal feature extraction in recent years. It can utilize noise and enhance weak signals. However, the traditional SR method has poor performance, and it is difficult to determine parameters of SR. Therefore, a new second-order tristable SR method (STSR) based on a new potential combining the classical bistable potential with Woods-Saxon potential is proposed in this paper. Firstly, the envelope signal of rolling bearings is the input signal of STSR. Then, the output of signal-to-noise ratio (SNR) is used as the fitness function of the Seeker Optimization Algorithm (SOA) in order to optimize the parameters of SR. Finally, the optimal parameters are used to set the STSR system in order to enhance and extract weak signals of rolling bearings. Simulated and experimental signals are used to demonstrate the effectiveness of STSR. The diagnosis results show that the proposed STSR method can obtain higher output SNR and better filtering performance than the traditional SR methods. It provides a new idea for fault diagnosis of rotating machinery.
Highlights
The rolling bearings are key components of rotary machines, but harsh working conditions often make them suffer from different failure, which may lead to the breakdown of the whole machinery and huge economic loss [1,2]
The output of signal-to-noise ratio (SNR) is used as the fitness function of the Seeker Optimization Algorithm (SOA) in order to optimize the parameters of Stochastic resonance (SR)
The analysis results of the bearing outer ring fault signal shows that the filtering performance of second-order tristable SR method (STSR) is significantly better than CSR and underdamped step-varying second-order SR method (USSSR)
Summary
The rolling bearings are key components of rotary machines, but harsh working conditions often make them suffer from different failure, which may lead to the breakdown of the whole machinery and huge economic loss [1,2]. It is of great significance to monitor the condition of the bearing. Vibration analysis has been widely applied to diagnose bearing faults. The faulty signal acquired from the bearing is usually weak or submerged in strong noise [3,4]. Traditional weak signal detection methods, such as empirical mode decomposition (EMD) [5], wavelets transform (WT) [6], singular value decomposition (SVD) [7], and variational mode decomposition (VMD) [8], mainly reduced noise to improve signal-to-noise ratio (SNR) and extract fault characteristics, which inevitably weakened useful fault signal characteristic information. SR can utilize noise to enhance weak signal energy
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