基于DHMM的轴承故障音频诊断方法

DHMM-based acoustic fault diagnosis method for bearings

轴承音频信号包含了大量的运行状态信息,与振动信号相比,音频信号的采集是非接触式的,具有使用方便和成本低廉等优势。通过提取机械轴承音频信号的Mel频率倒谱系数(MFCC)特征参数,并使用具有良好识别和抗噪性能的隐马尔可夫模型(HMM)分析轴承运行状态,首次将HMM对音频信号的分析方法应用于故障诊断。为了能够实现对轴承故障的实时诊断,采用计算量较小的离散HMM(DHMM)模型加快训练和识别速度。实验结果表明,该方法对轴承运行状态的识别正确率接近90%,识别时间约为31ms,效果较好,有效可行,具有很好的应用前景。

Acoustic signals emitted by beating contain lots of important information about its operation status.Compared with vibration signals,acoustic signals can be collected with non-contact sensors,so as to be convenient and cheap.By abstracting the mel-frequency-cepstrum-coefficients（MFCC） from acoustic signals of engine beating as the characteristic parameters,and using Hidden Markov Model（HMM） with the good performance of recognizing and anti-noise to analyze the operation status,this paper presents a new fault diagnosis method by acoustic signals based on HMM.In order to achieve diagnosis of beating fault in realtime,this paper adopts discrete HMM（DHMM） with low computational complexity to fasten the speed of training and identifying. Experiments results prove that,with an average recognition rate for all beating operating status of near 90% and recognition time of about 31ms,the presented method is effective and feasible and has a great prospect.