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Comparison of deep neural network architectures for fault detection in Tennessee Eastman process

Process monitoring and fault diagnosis methods are used to detect abnormal events in industrial processes. Process breakdowns hinder the overall productivity of the system which makes the early detection of faults very critical. Due to the highly non-linear nature of modern industrial processes, deep neural networks with several layers of non-linear complex representations fit aptly for contemporary fault diagnosis. Although deep neural networks have found wide array of application areas such as image recognition and speech recognition, their effectiveness in fault detection has not been tested substantially. In this study, a comparison between two deep neural network architectures, namely Deep Stacking Networks and Sparse Stacked Autoencoders for fault detection from process data is presented. The Tennessee Eastman benchmark process is considered to test the effectiveness of these deep architectures. A detailed comparison between the two architectures is illustrated with different hyperparameters. The experiment results show that the Sparse Stacked Autoencoders model has superior average fault detection capability and is also more stable as it has less variation in fault detection rate.