Hierarchical Deep Recurrent Neural Network based Method for Fault Detection and Diagnosis

TL;DR

This paper introduces a hierarchical deep recurrent neural network approach for fault detection and diagnosis in industrial systems, significantly improving detection accuracy for both incipient and non-incipient faults.

Contribution

It presents a novel hierarchical deep recurrent autoencoder neural network that leverages temporal process data and pseudo-random signals to enhance fault detection and classification.

Findings

Improved fault classification accuracy on Tennessee Eastman Process benchmark.

Effective detection of incipient faults using pseudo-random binary signals.

Hierarchical structure enhances detection performance over traditional methods.

Abstract

A Deep Neural Network (DNN) based algorithm is proposed for the detection and classification of faults in industrial plants. The proposed algorithm has the ability to classify faults, especially incipient faults that are difficult to detect and diagnose with traditional threshold based statistical methods or by conventional Artificial Neural Networks (ANNs). The algorithm is based on a Supervised Deep Recurrent Autoencoder Neural Network (Supervised DRAE-NN) that uses dynamic information of the process along the time horizon. Based on this network a hierarchical structure is formulated by grouping faults based on their similarity into subsets of faults for detection and diagnosis. Further, an external pseudo-random binary signal (PRBS) is designed and injected into the system to identify incipient faults. The hierarchical structure based strategy improves the detection and classification accuracy significantly for both incipient and non-incipient faults. The proposed approach is tested on the benchmark Tennessee Eastman Process resulting in significant improvements in classification as compared to both multivariate linear model-based strategies and non-hierarchical nonlinear model-based strategies.