基于故障特征时段识别的间歇过程故障诊断方法

间歇过程的多时段操作特性使得某一类型故障可能在一个或多个子操作时段具有明显表征,而在其他时段没有故障表征,即故障具有其相应的特征时段.提出了一种基于故障特征时段识别的故障诊断方法,通过对历史故障数据以及正常数据质心分布特征,识别历史故障的特征时段.利用多向Fisher判别分析(MFDA)方法分别建立对应的故障诊断模型,从而将故障诊断的搜索空间深入到特定的特征时段,提高了模型的诊断性能.仿真实验验证了该方法的可行性和有效性.

Fault Diagnosis Method for Batch Process Based on Identification of Fault Feature Phases

Because of the multiplicity of operation phases in batch process， faults may have obvious features in one or more specific operation phases， but do not show any features in other operation phases. That is， faults have corresponding feature phases. A fault diagnosis method based on identification of fault feature phases was proposed. Identification of fault feature phases was realized by comparing the differences between the centroids of historical faulty data set and the normal data set. Different fault diagnosis models were respectively developed based on multiway Fisher discriminant analysis (MFDA) to reduce the search space to specific fault feature phases and improve the diagnosis performance of the models. Simulation experimental results showed the feasibility and validity of the proposed method.