# Enhanced Fault Detection and Cause Identification Using Integrated Attention Mechanism

arXiv: https://arxiv.org/abs/2408.00033 · HTML: https://mlsanity.com/paper/2408.00033/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [STARNet: Sensor Trustworthiness and Anomaly Recognition via Approximated Likelihood Regret for Robust Edge Autonomy](https://mlsanity.com/paper/2309.11006/) (2023)
- [Concrete Surface Crack Detection with Convolutional-based Deep Learning Models](https://mlsanity.com/paper/2401.07124/) (2024)
- [The Correlations of Scene Complexity, Workload, Presence, and Cybersickness in a Task-Based VR Game](https://mlsanity.com/paper/2403.19019/) (2024)
- [Twin Transformer using Gated Dynamic Learnable Attention mechanism for Fault Detection and Diagnosis in the Tennessee Eastman Process](https://mlsanity.com/paper/2403.10842/) (2024)
- [Vehicle Routing Problem for Urban and Grey Zones Considering Heterogeneous Fleets and Carpooling](https://mlsanity.com/paper/2312.00905/) (2023)
- [Application of Deep Learning for Factor Timing in Asset Management](https://mlsanity.com/paper/2404.18017/) (2024)
- [Hierarchical SegNet with Channel and Context Attention for Accurate Lung Segmentation in Chest X-ray Images](https://mlsanity.com/paper/2405.12318/) (2024)
- [Constructing an Investment Fund through Stock Clustering and Integer Programming](https://mlsanity.com/paper/2407.05912/) (2024)

## Built on by (most cited first, 1 of 1)

- [Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning](https://mlsanity.com/paper/2605.09395/) (2026)

## Abstract (arXiv)

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.