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.
View on arXiv@article{ahang2025_2401.10266,
title={ Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies },
author={ Maryam Ahang and Todd Charter and Oluwaseyi Ogunfowora and Maziyar Khadivi and Mostafa Abbasi and Homayoun Najjaran },
journal={arXiv preprint arXiv:2401.10266},
year={ 2025 }
}