Abstract: In order to overcome the misclassification problems， multiway batch process monitoring based on the waveletbased multihidden Markov model tree (MHMT) was developed. MHMT can capture the clustering and persistence of the statistical characteristics for practical measured data. This approach provides less signal distortion and better understanding of the principal source of the system variability affecting the process. It solved the problem that discrete wavelet transform(DWT)can not obtain shiftinvariance，and then made simple modifications of the expansion structure， the method using stochastic model analysis the time domain expanded to timefrequency domain and extracted the main characteristics of the historical data， it is a good way to solve the multiway batch process monitoring problem. The proposed method was used to evaluate the industrial penicillin fermentation process data，the results clearly demonstrated the power and advantages of the proposed method in comparison with conventional MPCA method．

Key words: Batch process , Principal component analysis ,  Hidden Markov tree , Online monitoring

摘要： 为了克服多阶段间歇过程监控只针对时间尺度从而导致误报率过高的缺陷,建立了捕捉实际测量数
据的持续性和聚集性的隐马尔科夫树模型。该方法减少了信号扭曲从而更好地提取影响过程的系统变量,解决离
散小波变换不具有平移不变性的问题。对展开结构进行简单的修改,把时域扩展到时间灢频率域中,提取了历史数据
的主要特征,对多阶段间歇过程进行了有效监控。利用提出的方法对青霉素发酵过程进行监控,验证了该方法比传
统方法更为切实可行。

关键词: 间歇过程 , 主元分析 , 隐马尔可夫树 , 在线监控