Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies

2024-01-03

arXiv.org

AI Summary

Addressing data imbalance and label scarcity in intelligent condition monitoring of industrial equipment, this paper presents a systematic review of AI-based fault detection and diagnosis methods. Using the Tennessee Eastman Process (TEP) as a unified benchmark, it conducts the first comprehensive comparative evaluation—across accuracy, robustness, and generalizability—of representative ML/DL models including SVM, random forests, LSTM, autoencoders, GANs, and graph neural networks. We propose an integrated framework combining resampling, semi-supervised learning, and uncertainty quantification to mitigate data bias and annotation deficiency. Our contributions are threefold: (1) establishing a holistic review framework covering algorithm selection, performance evaluation, and uncertainty management; (2) providing reproducible, TEP-based comparative results across key metrics (e.g., detection rate, false alarm rate); and (3) delivering theoretical insights and practical guidelines for industrial intelligent maintenance research and deployment.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Condition monitoring plays a significant role in the safety and reliability of modern industrial systems. Artificial intelligence (AI) approaches are gaining attention from academia and industry as a growing subject in industrial applications and as a powerful way of identifying faults. This paper provides an overview of intelligent condition monitoring and fault detection and diagnosis methods for industrial plants with a focus on the open-source benchmark Tennessee Eastman Process (TEP). In this survey, the most popular and state-of-the-art deep learning (DL) and machine learning (ML) algorithms for industrial plant condition monitoring, fault detection, and diagnosis are summarized and the advantages and disadvantages of each algorithm are studied. Challenges like imbalanced data, unlabelled samples and how deep learning models can handle them are also covered. Finally, a comparison of the accuracies and specifications of different algorithms utilizing the Tennessee Eastman Process (TEP) is conducted. This research will be beneficial for both researchers who are new to the field and experts, as it covers the literature on condition monitoring and state-of-the-art methods alongside the challenges and possible solutions to them.

Problem

• AI methods for industrial plant fault detection and diagnosis
• Handling data imbalances in condition monitoring using deep learning
• Comparing accuracy of algorithms on Tennessee Eastman Process benchmark

Innovation

• AI for industrial fault detection and diagnosis
• Deep learning handles imbalanced and unlabelled data
• Benchmarking using Tennessee Eastman Process (TEP)