The authors investigate a multi-frequency signal which is decomposed failure by the traditional empirical mode decomposition (EMD) method. Moreover, the multi-frequency signal submerged in the coloured noise increases...
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The authors investigate a multi-frequency signal which is decomposed failure by the traditional empirical mode decomposition (EMD) method. Moreover, the multi-frequency signal submerged in the coloured noise increases the difficulty in signal decomposition. As a result, this noisy signal is decomposed unsuccessfully by the cooperation of the adaptive stochastic resonance (SR) in the classic bistable system and EMD. Then, a method combined adaptive SR in the periodic potential system and EMD is put forward to realise the decomposition. Meanwhile, the random particle swarm optimisation algorithm is applied to reach the optimal situation when signal-to-noise ratio attains the maximum value. Different simulation results verify the effectiveness of the proposed method. The proposed method might be useful in dealing with signal processing problems.
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 n...
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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.
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