FaultExplainer: Leveraging Large Language Models for Interpretable Fault Detection and Diagnosis

• URL: http://arxiv.org/abs/2412.14492v1
• Date: Thu, 19 Dec 2024 03:35:06 GMT
• Title: FaultExplainer: Leveraging Large Language Models for Interpretable Fault Detection and Diagnosis
• Authors: Abdullah Khan, Rahul Nahar, Hao Chen, Gonzalo E. Constante Flores, Can Li,
• Abstract summary: This paper presents FaultExplainer, an interactive tool designed to improve fault detection, diagnosis, and explanation in the Tennessee Eastman Process (TEP) FaultExplainer integrates real-time sensor data visualization, Principal Component Analysis (PCA)-based fault detection, and identification of top contributing variables within an interactive user interface powered by large language models (LLMs) We evaluate the LLMs' reasoning capabilities in two scenarios: one where historical root causes are provided, and one where they are not to mimic the challenge of previously unseen faults.
• Score: 7.161558367924948
• License:
• Abstract: Machine learning algorithms are increasingly being applied to fault detection and diagnosis (FDD) in chemical processes. However, existing data-driven FDD platforms often lack interpretability for process operators and struggle to identify root causes of previously unseen faults. This paper presents FaultExplainer, an interactive tool designed to improve fault detection, diagnosis, and explanation in the Tennessee Eastman Process (TEP). FaultExplainer integrates real-time sensor data visualization, Principal Component Analysis (PCA)-based fault detection, and identification of top contributing variables within an interactive user interface powered by large language models (LLMs). We evaluate the LLMs' reasoning capabilities in two scenarios: one where historical root causes are provided, and one where they are not to mimic the challenge of previously unseen faults. Experimental results using GPT-4o and o1-preview models demonstrate the system's strengths in generating plausible and actionable explanations, while also highlighting its limitations, including reliance on PCA-selected features and occasional hallucinations.

Related papers

• FD-LLM: Large Language Model for Fault Diagnosis of Machines [20.679299204776527]
  This study introduces a novel IFD approach by effectively adapting large language models to numerical data inputs for identifying faults from time-series sensor data. We propose FD-LLM, an LLM framework specifically designed for fault diagnosis by formulating the training of the LLM as a multi-class classification problem. We assess the fault diagnosis capabilities of four open-sourced LLMs based on the FD-LLM framework, and evaluate the models' adaptability and generalizability under various operational conditions.
  arXiv  Detail & Related papers  (2024-12-02T07:36:35Z)
• Unsupervised Model Diagnosis [49.36194740479798]
  This paper proposes Unsupervised Model Diagnosis (UMO) to produce semantic counterfactual explanations without any user guidance. Our approach identifies and visualizes changes in semantics, and then matches these changes to attributes from wide-ranging text sources.
  arXiv  Detail & Related papers  (2024-10-08T17:59:03Z)
• Interactively Diagnosing Errors in a Semantic Parser [7.136205674624813]
  We present work in progress on an interactive error diagnosis system for the CNLU. We show how the first two stages of the INLD pipeline can be cast as a model-based diagnosis problem. We demonstrate our system's ability to diagnose semantic errors on synthetic examples.
  arXiv  Detail & Related papers  (2024-07-08T21:16:09Z)
• Causal Disentanglement Hidden Markov Model for Fault Diagnosis [55.90917958154425]
  We propose a Causal Disentanglement Hidden Markov model (CDHM) to learn the causality in the bearing fault mechanism. Specifically, we make full use of the time-series data and progressively disentangle the vibration signal into fault-relevant and fault-irrelevant factors. To expand the scope of the application, we adopt unsupervised domain adaptation to transfer the learned disentangled representations to other working environments.
  arXiv  Detail & Related papers  (2023-08-06T05:58:45Z)
• Exposing Attention Glitches with Flip-Flop Language Modeling [55.0688535574859]
  This work identifies and analyzes the phenomenon of attention glitches in large language models. We introduce flip-flop language modeling (FFLM), a family of synthetic benchmarks designed to probe the extrapolative behavior of neural language models. We find that Transformer FFLMs suffer from a long tail of sporadic reasoning errors, some of which we can eliminate using various regularization techniques.
  arXiv  Detail & Related papers  (2023-06-01T17:44:35Z)
• System Resilience through Health Monitoring and Reconfiguration [56.448036299746285]
  We demonstrate an end-to-end framework to improve the resilience of man-made systems to unforeseen events. The framework is based on a physics-based digital twin model and three modules tasked with real-time fault diagnosis, prognostics and reconfiguration.
  arXiv  Detail & Related papers  (2022-08-30T20:16:17Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine [54.650189434544146]
  A data-driven fault diagnosis and isolation scheme is explicitly developed for failure in the fuel supply system and sensor measurements. A model is trained using machine learning classifiers to detect a given set of fault scenarios in real-time on which it is trained. Several simulation studies were carried out to demonstrate and illustrate the proposed fault diagnosis scheme's advantages, capabilities, and performance.
  arXiv  Detail & Related papers  (2021-10-17T13:42:37Z)
• Bayesian Structural Learning for an Improved Diagnosis of Cyber-Physical Systems [0.8379286663107844]
  This paper proposes a scalable algorithm for an automated learning of a structured diagnosis model. It offers equal performance to comparable algorithms while giving better interpretability.
  arXiv  Detail & Related papers  (2021-04-02T11:14:05Z)
• An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery [2.055054374525828]
  This paper proposes a new approach for fault detection and diagnosis in rotating machinery. The methodology consists of three parts: feature extraction, fault detection and fault diagnosis. The effectiveness of the proposed approach is shown on three datasets containing different mechanical faults.
  arXiv  Detail & Related papers  (2021-02-23T18:28:18Z)
• Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults? [9.1591191545173]
  Intermediate-Severity (IS) faults present milder symptoms compared to severe faults. The lack of IS fault examples in the training data can pose severe risks to Fault Detection and Diagnosis (FDD) methods. We discuss how to design more effective ensemble models for detecting and diagnosing IS faults.
  arXiv  Detail & Related papers  (2020-07-07T02:05:04Z)

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.