基于VbHMM的机械故障源数估计方法研究

Yajing Zhu, Zhinong Li, Jingzhi Tu
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引用次数: 0

摘要

传统的信号源数估计方法必须保证信号源的独立性和无噪声干扰。针对传统BSS方法存在的上述不足,将变分贝叶斯隐马尔可夫模型(VbHMM)与自相关判断(ARD)相结合,提出了一种基于变分贝叶斯隐马尔可夫模型的机械故障源数估计方法。该方法在引入贝叶斯网络后,利用马尔可夫模型(HMM)捕捉动态非线性信号中一系列与时间相关的时间序列信息的特征。利用贝叶斯推理和自相关判断(ARD)的独特模型比较函数,推导出非平稳信号中隐藏源的最优个数。仿真和实验结果验证了该方法的有效性。
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Research on Estimation Method of Mechanical Fault Source Number Based on VbHMM
The traditional source number estimation method must ensure that the signal sources are independent and noise-free interference. Based on the above deficiency in the traditional BSS method, combining variational Bayesian hidden Markov model (VbHMM) and autocorrelation determination (ARD), a estimation method of mechanical fault sources number based on VbHMM is proposed. In the proposed method, after the Bayesian networks are introduced, the Markov models (HMM) is used to capture the characteristics of a series of time-related time series information in the dynamic and nonlinear signals. The optimal number of hidden sources in the non-stationary signal is deduced by the unique model comparison function of Bayesian inference and autocorrelation determination (ARD). Simulation and experimental results verify the effectiveness of the proposed method.
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