连续隐半马尔科夫模型在轴承性能退化评估中的应用

连续隐半Markov模型(Continuous hidden semi-Markov model,CHSMM)是隐Markov模型(Hidden Markov model,HMM)的一种扩展形式,可用于时间序列过程的动态建模。通过加入状态分布参数并对多组观测值进行连续化,可加强模型对新观测值的处理能力以及对状态驻留时间的建模能力。利用该方法建立了轴承性能退化的评估模型。首先,分析振动信号并提取频带能量作为退化特征;然后将正常状态下的特征样本作为模型的观测值对CHSMM进行训练;最后将待测的特征样本输入模型,得到待测样本相对于所建立正常模型的输出概率,作为轴承性能退化状态的标志。轴承疲劳寿命试验结果表明:所提的评估模型能较好地刻画轴承性能退化的过程,并能在早期对轴承的性能退化做出预警。

Application of Continuous Hidden Semi-Markov Model in Bearing Performance Degradation Assessment

Continuous hidden semi-Markov model (CHSMM) is an extension of hidden Markov model (HMM), and it can be used to model time series process dynamically. It is capable of processing a new observation and modeling the time duration of hidden states by using a continuous observations density function and estimating the state duration parameters. In this paper, a model based on the CHSMM was constructed to assess the bearing performance degradation. First, the frequency band energy was extracted as the degradation indicators from the vibration signal. Second, the CHSMM was trained by the feature samples under normal conditions. Then, the test samples were input into this health assessment model, and their output probability was obtained. The difference between this probability and that of normal samples could be regarded as an index of degradation. Experiment results on the bearing performance degradation test indicated that, the proposed model can depict the degradation process effectively, and predict the occurrence of some incipient faults.