Twin Transformer using Gated Dynamic Learnable Attention mechanism for Fault Detection and Diagnosis in the Tennessee Eastman Process

• URL: http://arxiv.org/abs/2403.10842v3
• Date: Fri, 21 Jun 2024 07:04:49 GMT
• Title: Twin Transformer using Gated Dynamic Learnable Attention mechanism for Fault Detection and Diagnosis in the Tennessee Eastman Process
• Authors: Mohammad Ali Labbaf-Khaniki, Mohammad Manthouri,
• Abstract summary: Fault detection and diagnosis (FDD) is a crucial task for ensuring the safety and efficiency of industrial processes. We propose a novel FDD methodology for the Tennessee Eastman Process (TEP), a widely used benchmark for chemical process control. A novel attention mechanism, Gated Dynamic Learnable Attention (GDLAttention), is introduced which integrates a gating mechanism and dynamic learning capabilities.
• Score: 0.46040036610482665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fault detection and diagnosis (FDD) is a crucial task for ensuring the safety and efficiency of industrial processes. We propose a novel FDD methodology for the Tennessee Eastman Process (TEP), a widely used benchmark for chemical process control. The model employs two separate Transformer branches, enabling independent processing of input data and potential extraction of diverse information. A novel attention mechanism, Gated Dynamic Learnable Attention (GDLAttention), is introduced which integrates a gating mechanism and dynamic learning capabilities. The gating mechanism modulates the attention weights, allowing the model to focus on the most relevant parts of the input. The dynamic learning approach adapts the attention strategy during training, potentially leading to improved performance. The attention mechanism uses a bilinear similarity function, providing greater flexibility in capturing complex relationships between query and key vectors. In order to assess the effectiveness of our approach, we tested it against 21 and 18 distinct fault scenarios in TEP, and compared its performance with several established FDD techniques. The outcomes indicate that the method outperforms others in terms of accuracy, false alarm rate, and misclassification rate. This underscores the robustness and efficacy of the approach for FDD in intricate industrial processes.

Related papers

• Joint Input and Output Coordination for Class-Incremental Learning [84.36763449830812]
  We propose a joint input and output coordination (JIOC) mechanism to address these issues. This mechanism assigns different weights to different categories of data according to the gradient of the output score. It can be incorporated into different incremental learning approaches that use memory storage.
  arXiv  Detail & Related papers  (2024-09-09T13:55:07Z)
• Enhanced Fault Detection and Cause Identification Using Integrated Attention Mechanism [0.3749861135832073]
  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. 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.
  arXiv  Detail & Related papers  (2024-07-31T12:01:57Z)
• BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment [34.60961915466469]
  A brain diffuser with hierarchical transformer (BDHT) is proposed to estimate effective connectivity for mild cognitive impairment (MCI) analysis. The proposed model achieves superior performance in terms of accuracy and robustness compared to existing approaches.
  arXiv  Detail & Related papers  (2023-12-14T15:12:00Z)
• A Novel Transfer Learning Method Utilizing Acoustic and Vibration Signals for Rotating Machinery Fault Diagnosis [12.631120583797518]
  Fault diagnosis of rotating machinery plays a important role for the safety and stability of modern industrial systems. There is a distribution discrepancy between training data and data of real-world operation scenarios. This paper proposed a transfer learning based method utilizing acoustic and vibration signal to address this distribution discrepancy.
  arXiv  Detail & Related papers  (2023-10-20T10:50:14Z)
• A Discrepancy Aware Framework for Robust Anomaly Detection [51.710249807397695]
  We present a Discrepancy Aware Framework (DAF), which demonstrates robust performance consistently with simple and cheap strategies. Our method leverages an appearance-agnostic cue to guide the decoder in identifying defects, thereby alleviating its reliance on synthetic appearance. Under the simple synthesis strategies, it outperforms existing methods by a large margin. Furthermore, it also achieves the state-of-the-art localization performance.
  arXiv  Detail & Related papers  (2023-10-11T15:21:40Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• Transfer Learning for Autonomous Chatter Detection in Machining [0.9281671380673306]
  Large-amplitude chatter vibrations are one of the most important phenomena in machining processes. Three challenges can be identified in applying machine learning for chatter detection at large in industry. These three challenges can be grouped under the umbrella of transfer learning.
  arXiv  Detail & Related papers  (2022-04-11T20:46:06Z)
• Explainability: Relevance based Dynamic Deep Learning Algorithm for Fault Detection and Diagnosis in Chemical Processes [0.0]
  Two important applications of Statistical Process Control (SPC) in industrial settings are fault detection and diagnosis (FDD) In this work a deep learning (DL) based methodology is proposed for FDD. We investigate the application of an explainability concept to enhance the FDD accuracy of a deep neural network model trained with a data set of relatively small number of samples.
  arXiv  Detail & Related papers  (2021-03-22T23:10:05Z)
• Anomaly Detection Based on Selection and Weighting in Latent Space [73.01328671569759]
  We propose a novel selection-and-weighting-based anomaly detection framework called SWAD. Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD.
  arXiv  Detail & Related papers  (2021-03-08T10:56:38Z)
• Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment [60.31418655784291]
  We propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i.e., both kinematic and visual data, from simulator to real robot. It remedies the domain gap with enhanced transferable features by using temporal cues in videos, and inherent correlations in multi-modal towards recognizing gesture. Results show that our approach recovers the performance with great improvement gains, up to 12.91% in ACC and 20.16% in F1score without using any annotations in real robot.
  arXiv  Detail & Related papers  (2021-03-06T09:10:03Z)
• Towards Efficient Processing and Learning with Spikes: New Approaches for Multi-Spike Learning [59.249322621035056]
  We propose two new multi-spike learning rules which demonstrate better performance over other baselines on various tasks. In the feature detection task, we re-examine the ability of unsupervised STDP with its limitations being presented. Our proposed learning rules can reliably solve the task over a wide range of conditions without specific constraints being applied.
  arXiv  Detail & Related papers  (2020-05-02T06:41:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.