Dynamic convolutional gated recurrent unit attention auto-encoder for feature learning and fault detection in dynamic industrial processes

The dynamic characteristics (i.e. autocorrelation and cross-correlation) in modern industrial systems have raised great challenges to process fault detection. To cope with the dynamics and uncertainty of dynamic industrial processes, this paper proposes a new process control method, dynamic convolutional gated recurrent unit attention auto-encoder (DCGRUA-AE) for fault detection in dynamic processes. Firstly, DCGRUA-AE integrates a convolutional gated recurrent unit (CGRU) with a local convolution layer to learn both global and local features of dynamic process data in an unsupervised fashion. Secondly, a dual attention module is embedded in the deep network to preserve effective features. Finally, DeconvGRU combined with a dense layer is used as the encoder to reconstruct the original process data. Two statistics (i.e. T-square (T-2) and squared prediction error (SPE)) based on DCGRUA-AE are used to set the control limits for fault detection. The feasibility and superiority of DCGRUA-AE-based fault detection method have been verified on four industrial processes. The experimental results indicate that the CGRU and dual attention mechanism can significantly improve the fault detection performance of DCGRUA-AE in dynamic processes. The hybrid of CGRU, attention mechanism and auto-encoder provides a new method for fault detection in dynamic industrial processes.

Automated summary

This paper proposes a new method, DCGRUA-AE, for fault detection in dynamic industrial processes. The method integrates CGRU, attention mechanism, and auto-encoder to learn both global and local features of dynamic process data. The feasibility and superiority of the method have been verified through experiments on four industrial processes.