Abstract：A method of weak features detection for mechanical fault data is proposed, which combined the stochastic resonance and the improved Shannon entropy of symbolic series. Firstly, the basic principles of SR and symbolic sequence analysis (SSA) is briefly introduced. Secondly, symbolic sequence Shannon entropy of output signal is the measure whether the state of this system of SR is the best one. Finally, according to adjust system parameters a and b adaptively, the system of SR achieves the best state and analyzes signal to noise ratio (SNR) and spectrum of output signal. Information of output signal show that Shannon entropy of symbolic series can reflect the state of SR , the best SNR of output signal can be achieved and it can be easily used in practical projects, which confirms the new method proposed is correct and useful.
潘峥嵘,谯自健,张宁. 基于符号序列熵的自适应随机共振的微弱信号检测[J]. 计量学报, 2015, 36(5): 496-500.
PAN Zheng-rong,QIAO Zi-jian,ZHANG Ning. Weak Signal Detection of Adaptive Stochastic Resonance Based on Shannon Entropy of Symbolic Series. Acta Metrologica Sinica, 2015, 36(5): 496-500.
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2015, Vol. 36 
