# Multi-Fault Diagnosis Of Industrial Rotating Machines Using Data-Driven Approach: A Review Of Two Decades Of Research

arXiv: https://arxiv.org/abs/2206.14153 · HTML: https://mlsanity.com/paper/2206.14153/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 163 references that are in the map.
- First posted on arXiv 30 May 2022.

## Builds on (most cited first, 13 of 13)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Explainable Artificial Intelligence: a Systematic Review](https://mlsanity.com/paper/2006.00093/) (2020)
- [Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications](https://mlsanity.com/paper/2005.02144/) (2020)
- [Multimodal Co-learning: Challenges, Applications with Datasets, Recent Advances and Future Directions](https://mlsanity.com/paper/2107.13782/) (2021)
- [Explainable Predictive Process Monitoring](https://mlsanity.com/paper/2008.01807/) (2020)
- [Failure Modes in Machine Learning Systems](https://mlsanity.com/paper/1911.11034/) (2019)
- [Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics -- A Comprehensive Review](https://mlsanity.com/paper/1901.08247/) (2019)
- [Machine Learning-Based Unbalance Detection of a Rotating Shaft Using Vibration Data](https://mlsanity.com/paper/2005.12742/) (2020)
- [The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions](https://mlsanity.com/paper/2104.02425/) (2021)
- [Artificial Intelligence in Intelligent Tutoring Robots: A Systematic Review and Design Guidelines](https://mlsanity.com/paper/1903.03414/) (2019)
- [Data Validation](https://mlsanity.com/paper/2012.12028/) (2020)
- [Adversarial Machine Learning Attacks on Condition-Based Maintenance Capabilities](https://mlsanity.com/paper/2101.12097/) (2021)

## Built on by (most cited first, 2 of 2)

- [Bearing fault diagnosis based on multi-scale spectral images and convolutional neural network](https://mlsanity.com/paper/2503.21566/) (2025)
- [AI-Powered Machine Learning Approaches for Fault Diagnosis in Industrial Pumps](https://mlsanity.com/paper/2508.15550/) (2025)

## Abstract (arXiv)

Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of machinery. Majority of these machines comprise rotating components and are called rotating machines. The engineers' top priority is to maintain these critical machines to reduce the unplanned shutdown and increase the useful life of machinery. Predictive maintenance (PDM) is the current trend of smart maintenance. The challenging task in PDM is to diagnose the type of fault. With Artificial Intelligence (AI) advancement, data-driven approach for predictive maintenance is taking a new flight towards smart manufacturing. Several researchers have published work related to fault diagnosis in rotating machines, mainly exploring a single type of fault. However, a consolidated review of literature that focuses more on multi-fault diagnosis of rotating machines is lacking. There is a need to systematically cover all the aspects right from sensor selection, data acquisition, feature extraction, multi-sensor data fusion to the systematic review of AI techniques employed in multi-fault diagnosis. In this regard, this paper attempts to achieve the same by implementing a systematic literature review on a Data-driven approach for multi-fault diagnosis of Industrial Rotating Machines using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method. The PRISMA method is a collection of guidelines for the composition and structure of systematic reviews and other meta-analyses. This paper identifies the foundational work done in the field and gives a comparative study of different aspects related to multi-fault diagnosis of industrial rotating machines. The paper also identifies the major challenges, research gap. It gives solutions using recent advancements in AI in implementing multi-fault diagnosis, giving a strong base for future research in this field.