Bidirectional deep recurrent neural networks for process fault classification

Abstract

In this study, a new approach for time series based condition monitoring and fault diagnosis based on bidirectional recurrent neural networks is presented. The application of bidirectional recurrent neural networks essentially provide a viewpoint change on the fault diagnosis task, which allows to handle fault relations over longer time horizons helping in avoiding critical process breakdowns and increasing the overall productivity of the system. To further enhance the capability, we propose a novel procedure of data preprocessing and restructuring which enforces the generalization and a more efficient data utilization and consequently yields more efficient network training, especially for sequential fault classification task. The proposed Bidirectional Long Short Term Memory network outperforms standard recurrent architectures including vanilla recurrent neural networks, Long Short Term Memories and Gated Recurrent Units. We apply the proposed approach to the Tennessee Eastman benchmark process to test the effectiveness of the mentioned deep architectures and provide a detailed comparative analysis. The experimental results for binary as well as multi-class classification show the superior average fault detection capability of the bidirectional Long Short Term Memory Networks compared to the other architectures and to results from other state-of-the-art architectures found in the literature.

Keywords: Bidirectional long-short term memory; Condition monitoring; Deep learning; Fault detection and classification; Recurrent neural networks; Time series analysis.