Summary of Data-driven Machinery Fault Diagnosis: a Comprehensive Review, by Dhiraj Neupane et al.
Data-driven Machinery Fault Diagnosis: A Comprehensive Review
by Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley, Sunil Aryal
First submitted to arxiv on: 29 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 A machine learning-based Machinery Fault Diagnosis (MFD) survey is presented, focusing on various types of machine learning approaches for detecting and diagnosing machinery faults in industrial settings. The paper reviews existing literature, highlighting strengths and limitations, and discusses challenges associated with implementing data-driven MFD solutions, such as dealing with noisy data and adapting models to accommodate new or unforeseen faults. A comprehensive review of condition-based analysis methods and available fault datasets is also provided, along with recommendations for mitigating implementation challenges and future research prospects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machinery Fault Diagnosis (MFD) is crucial in manufacturing for safe and efficient operation. This survey reviews machine learning approaches used to detect and diagnose various machinery faults. It highlights the strengths and limitations of different methods, discusses challenges like noisy data and adapting models, and reviews condition-based analysis methods and fault datasets. The paper aims to help researchers develop this field. |
Keywords
» Artificial intelligence » Machine learning
