Dual attention bidirectional generative adversarial network for dynamic uncertainty process monitoring and diagnosis

Abstract

In industrial process monitoring, uncertainty in a system arises when measured data are not representative of actual data. Uncertain information should be extracted to maintain safe manufacturing. Interval value methods are generally used to monitor uncertainty in systems, and these methods assume the same uncertainty among variables. However, the variables of the same process differ in uncertainty. Moreover, the dynamic nature of the system causes a more complex uncertainty. To monitor and diagnose such processes, a fault-sensitive bidirectional generative adversarial network (FSBiGAN) is proposed. First, to extract valuable characteristics from uncertain input, BiGANs with self-attentive mechanisms are put in place. Second, a sensitive variable selection approach is utilized to create new fault-sensitive indicators capable of adapting to the dynamic changes of each process. Third, the variable selection approach is applied once more to identify variables causing a fault to occur. Finally, the self-attentive and sensitive variable-picking algorithms can be combined with other generative models to identify defects and analyze the dynamic properties of the process. Comparative experiments applying traditional monitoring and interval methods on the Tennessee Eastman dataset injected with differential uncertainty validate the effectiveness of FSBiGAN in monitoring and diagnosing dynamic uncertainty systems.