# Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect

arXiv: https://arxiv.org/abs/2010.03207 · HTML: https://mlsanity.com/paper/2010.03207/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [RobustPdM: Designing Robust Predictive Maintenance against Adversarial Attacks](https://mlsanity.com/paper/2301.10822/) (2023)
- [Bidirectional Reactive Programming for Machine Learning](https://mlsanity.com/paper/2311.16977/) (2023)
- [A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression](https://mlsanity.com/paper/2506.20090/) (2025)
- [SARNet: A Spike-Aware consecutive validation Framework for Accurate Remaining Useful Life Prediction](https://mlsanity.com/paper/2510.22955/) (2025)

## Abstract (arXiv)

Given the growing amount of industrial data spaces worldwide, deep learning solutions have become popular for predictive maintenance, which monitor assets to optimise maintenance tasks. Choosing the most suitable architecture for each use-case is complex given the number of examples found in literature. This work aims at facilitating this task by reviewing state-of-the-art deep learning architectures, and how they integrate with predictive maintenance stages to meet industrial companies' requirements (i.e. anomaly detection, root cause analysis, remaining useful life estimation). They are categorised and compared in industrial applications, explaining how to fill their gaps. Finally, open challenges and future research paths are presented.