Summary of Intelligent Condition Monitoring Of Industrial Plants: An Overview Of Methodologies and Uncertainty Management Strategies, by Maryam Ahang et al.
Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies
by Maryam Ahang, Todd Charter, Oluwaseyi Ogunfowora, Maziyar Khadivi, Mostafa Abbasi, Homayoun Najjaran
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper provides an overview of intelligent condition monitoring and fault detection/diagnosis methods for industrial plants using open-source benchmark Tennessee Eastman Process (TEP). It summarizes popular and state-of-the-art deep learning (DL) and machine learning (ML) algorithms, highlighting their advantages and disadvantages. The study also covers challenges like imbalanced data and unlabelled samples, as well as how deep learning models can handle them. A comparison of algorithm accuracies and specifications using TEP is conducted. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps keep industrial systems safe and reliable by showing how artificial intelligence (AI) works best for identifying faults. It looks at popular AI methods for monitoring industrial plants, including those that use deep learning (DL) or machine learning (ML). The study also talks about the challenges these methods face, like dealing with imbalanced data or unlabelled samples. By understanding these methods and challenges, researchers and experts can make better decisions. |
Keywords
* Artificial intelligence * Deep learning * Machine learning
