基于HMM和BN的精轧过程故障传播路径识别

Propagation path identification on faults in finishing rolling process based onHMM and BN

针对故障传播路径识别领域中基于数据的方法会造成变量间存在大量冗余连接的问题和基于知识的方法会造成变量间信息丢失的问题,提出隐马尔科夫模型与贝叶斯网络相结合的故障传播路径识别新方法。首先,将带钢热连轧过程知识构建为定性贝叶斯网络结构,通过主成分分析方法对带钢热连轧过程中的数据进行降维处理,以得到训练模型所需的观测序列;然后,根据降维后的正常历史数据及其对数似然值,建立贝叶斯网络进行传播路径识别所需的条件概率表;最后,将故障数据及其对数似然值作为贝叶斯网络进行识别故障传播路径的似然证据。实验结果表明,该方法能精准定位发生故障的6个变量,没有出现误诊和漏检的现象,且能准确识别故障的传播路径。

For the problems that there were a large number of redundant connections between the variables caused by data-based methods and the information loss between the variables caused by knowledge-based methods in the field of the propagation path identification on faults,a new method for the propagation path identification on faults that combined Hidden Markov Model(HMM)and Bayesian Network(BN)was proposed.First,the knowledge of the hot strip rolling process was constructed as a qualitative BN structure,and the dimensionality of the data in the hot strip rolling process was reduced by the principal component analysis method to obtain the observation sequence required for training model.Then,based on the normal historical data after dimensionality reduction and its log-likelihood value,the conditional probability table was established for BN to identify the propagation path.Finally,the fault data and their log-likelihood values were used as the likelihood evidence for BN to identify the fault propagation path.The experimental results show that this method can accurately locate the six variables where the fault occurs,without any misdiagnosis or missed detection,and can accurately identify the propagation path of faults.