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

arXiv: https://arxiv.org/abs/2401.10266 · HTML: https://mlsanity.com/paper/2401.10266/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 161 references that are in the map.
- First posted on arXiv 3 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Fusing Physics-based and Deep Learning Models for Prognostics](https://mlsanity.com/paper/2003.00732/) (2020)
- [A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources](https://mlsanity.com/paper/2007.12269/) (2020)
- [Fault Detection and Identification using Bayesian Recurrent Neural Networks](https://mlsanity.com/paper/1911.04386/) (2019)
- [Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN](https://mlsanity.com/paper/2206.12076/) (2022)
- [Explainability: Relevance based Dynamic Deep Learning Algorithm for Fault Detection and Diagnosis in Chemical Processes](https://mlsanity.com/paper/2103.12222/) (2021)
- [Neural Component Analysis for Fault Detection](https://mlsanity.com/paper/1712.04118/) (2017)

## Built on by (most cited first, 3 of 3)

- [Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework](https://mlsanity.com/paper/2404.05891/) (2024)
- [MaaSO: SLO-aware Orchestration of Heterogeneous Model Instances for MaaS](https://mlsanity.com/paper/2509.06362/) (2025)
- [A Hybrid Intelligent Framework for Uncertainty-Aware Condition Monitoring of Industrial Systems](https://mlsanity.com/paper/2604.09932/) (2026)

## Abstract (arXiv)

Condition monitoring is essential for ensuring the safety, reliability, and efficiency of modern industrial systems. With the increasing complexity of industrial processes, artificial intelligence (AI) has emerged as a powerful tool for fault detection and diagnosis, attracting growing interest from both academia and industry. This paper provides a comprehensive overview of intelligent condition monitoring methods, with a particular emphasis on chemical plants and the widely used Tennessee Eastman Process (TEP) benchmark. State-of-the-art machine learning (ML) and deep learning (DL) algorithms are reviewed, highlighting their strengths, limitations, and applicability to industrial fault detection and diagnosis. Special attention is given to key challenges, including imbalanced and unlabeled data, and to strategies by which models can address these issues. Furthermore, comparative analyses of algorithm performance are presented to guide method selection in practical scenarios. This survey is intended to benefit both newcomers and experienced researchers by consolidating fundamental concepts, summarizing recent advances, and outlining open challenges and promising directions for intelligent condition monitoring in industrial plants.