摘要
隐马尔可夫模型(Hidden Markov model,HMM)是一种具有较强的时间序列建模能力的信号模式处理工具, 在语音处理中获得了广泛应用,特别适合于非线性、重复再现性不佳的信号的分析。基于振动信号与语音信号的相似性,将CHMM(Continuous Hidden Markov model)引入了旋转机械的故障诊断中。采用12阶LPC倒谱系数进行特征提取,建立CHMM,为防止数据下溢,引入前向一后向比例因子算法求其对数似然概率,并且采用K-means 算法对CHMM进行参数初始化。在给定的观测序列中每一种模型的优化路径通过Viterbi算法实现,用Baum-Welch 算法实现参数重估,并给出了重估公式。最后,在转子试验台上模拟了四种故障试验,建立了四种故障的CHMM 模型,通过求其最大似然概率值来决定机器的运行状态,试验结果证明了该方法的有效性。
Hidden Markov model（HMM） as a tool for disposing signal pattern which has great ability of building time sequence, has widely been used in speech recognition. It is especially fit for signal which is nonlinear, non-stationary, bad in repeating to analysis. Based on the comparability between vibration signal and sound signal, CHMM is introduced to fault diagnosis for rotating machine. CHMM is built by using 12 rank LPC cepstrum coefficient to extract feature vectors, scaled forwards-backwards algorithm is introduced to calculate log-likelihood avoiding the data to underflow and K-means algorithm is also used to initialize the parameter. In the given observation sequence, optimizing every model with Viterbi algorithm, with baum-welch algorithm to re-estimate parameter, and the re-estimation formula is also provided. Last, four kinds of fault experiment have been simulated on the rotor test-bed, and four kinds of fault CHMM model are built. Machine＇s operating state is determined by calculating the maximal log-likelihood, and the results of experiment proves that this kind of method is effective.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2006年第5期126-130,共5页
Journal of Mechanical Engineering
基金
国家自然科学基金资助项目(50275024)。
关键词
CHMM
故障诊断
旋转机械
模式识别
CHMM
Faults diagnosis
Rotating machine
Pattern recognition
