关键词: 轴承, 故障诊断, 连续高斯混合密度隐马尔可夫模型, 音频信号

Abstract: Plentiful significant information about the operation status of bearings，which is potential for the fault diagnose after processed properly，is contained in their acoustic signals.In this paper，a new fault diagnosis scheme using acoustic signals is proposed for the bearings by introducing Continuous Gaussian mixture Hidden Markov Model（CGHMM） method，in which the data processing error due to vector quantization is avoided，and therefore the diagnosis precision is improved.Besides，a clustering algorithm and a scaled coefficient algorithm are introduced for parameters initiation and the forward and backward algorithms to simplify the complexity in the computation and improve the training and recognizing speed and diagnosis precision.At last，experiment results of a diagnosis precision achieve to 98.75% and demonstrate the feasibility and potential for applications of the presented scheme.

Key words: bearing, fault diagnosis, Continuous Gaussian mixture Hidden Markov Model（CGHMM）, acoustic signal