Fault diagnosis of batch process based on improved GA-SeparableConv1D-TCN network

Batch process is an indispensable part of modern industrial production. Because of its flexibility and high efficiency, it is widely used in biopharmaceutical, wastewater treatment, fine chemical and other fields. Due to the improvement of modern equipment and the expansion of the scale of the factory, the chance of accidents and failures also showed an exponential increase trend. Therefore, fault diagnosis is very important to ensure the stability and safety of chemical processes. In order to improve the safety and reliability of batch process, an improved Genetic Algorithm is proposed to optimize the parameters of Separable Convolution Network and Temporal Convolutional Network (SeparableConv1D-TCN) for fault diagnosis of batch process. Firstly, the SeparableConv1D-TCN network is constructed to extract features from the original intermittent data and perform fault diagnosis by standardizing the intermittent data. In order to find the network parameters with high diagnostic accuracy, the genetic algorithm with good point set method and optimal domain search is used to optimize. The effectiveness of the method is verified by simulation experiments and comparative experiments with penicillin experimental data.