Enhanced Fault Detection and Cause Identification Using Integrated Attention Mechanism

TL;DR

This paper presents an advanced fault detection method for the Tennessee Eastman Process using a BiLSTM neural network combined with an integrated attention mechanism, improving accuracy and interpretability.

Contribution

The study introduces a novel integrated attention mechanism within a BiLSTM framework for fault detection, enhancing pattern recognition and interpretability in process monitoring.

Findings

Superior accuracy in fault detection compared to existing methods

Reduced false alarm and misclassification rates

Effective identification of fault causes in TEP

Abstract

This study introduces a novel methodology for fault detection and cause identification within the Tennessee Eastman Process (TEP) by integrating a Bidirectional Long Short-Term Memory (BiLSTM) neural network with an Integrated Attention Mechanism (IAM). The IAM combines the strengths of scaled dot product attention, residual attention, and dynamic attention to capture intricate patterns and dependencies crucial for TEP fault detection. Initially, the attention mechanism extracts important features from the input data, enhancing the model's interpretability and relevance. The BiLSTM network processes these features bidirectionally to capture long-range dependencies, and the IAM further refines the output, leading to improved fault detection results. Simulation results demonstrate the efficacy of this approach, showcasing superior performance in accuracy, false alarm rate, and misclassification rate compared to existing methods. This methodology provides a robust and interpretable solution for fault detection and diagnosis in the TEP, highlighting its potential for industrial applications.