Fault diagnosis based on DA-CycleGAN for multimode chemical processes
In modern chemical processes,timely and accurate fault diagnosis is important for enhancing the safety and reliability.Data-driven fault diagnosis methods have been regarded as a promising approach in the last decades of research for increasingly complex chemical processes.Data-driven fault diagnosis methods can greatly reduce the dependence on human experience,and realize end-to-end fault diagnosis by automatically extracting features.However,most existing research assumes training and testing data come from the same distribution,while a chemical process may have multiple working conditions.On the one hand,the fault diagnosis performance of the model will deteriorate when the process is run under new working conditions.On the other hand,due to the low probability of failure,some operating conditions may have few fault data in history.To address these issues,in this work,a novel fault diagnosis method,DA-CycleGAN,is proposed for multimode chemical processes.This study is the first to overcome the degradation of model diagnosis performance when only normal data are available under new working conditions.It notes that the normal data is available under any working condition.A two-dimensional CycleGAN is used to capture the temporal and spatial features of fault data.And fault data is generated by combining fault features and normal data under new operating conditions,thus filling a blank in new working conditions for fault data.Furthermore,the domain adaptation method is used to minimize the distribution differences between historical fault data and generated data and to improve the fault diagnostic performance under new operating conditions.To test the performance of this method,four working conditions of the Tennessee-Easthman(TE)process are used in the experiment.The results on twelve condition-changed fault diagnosis tasks show that this method can improve the average fault diagnosis rate by more than 3％compared to the model trained using only historical fault data.
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