# A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression

arXiv: https://arxiv.org/abs/2506.20090 · HTML: https://mlsanity.com/paper/2506.20090/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [An Explainable Regression Framework for Predicting Remaining Useful Life of Machines](https://mlsanity.com/paper/2204.13574/) (2022)
- [Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect](https://mlsanity.com/paper/2010.03207/) (2020)
- [System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis](https://mlsanity.com/paper/2005.05239/) (2020)
- [A dissimilarity-based approach to predictive maintenance with application to HVAC systems](https://mlsanity.com/paper/1701.03633/) (2017)
- [Revolutionizing System Reliability: The Role of AI in Predictive Maintenance Strategies](https://mlsanity.com/paper/2404.13454/) (2024)
- [RUL forecasting for wind turbine predictive maintenance based on deep learning](https://mlsanity.com/paper/2412.17823/) (2024)

## Abstract (arXiv)

Predictive maintenance (PdM) has become a crucial element of modern industrial practice. PdM plays a significant role in operational dependability and cost management by decreasing unforeseen downtime and optimizing asset life cycle management. Machine learning and deep learning have enabled more precise forecasts of equipment failure and remaining useful life (RUL). Although many studies have been conducted on PdM, there has not yet been a standalone comparative study between regression- and classification-based approaches. In this review, we look across a range of PdM methodologies, while focusing more strongly on the comparative use of classification and regression methods in prognostics. While regression-based methods typically provide estimates of RUL, classification-based methods present a forecast of the probability of failure across defined time intervals. Through a comprehensive analysis of recent literature, we highlight key advancements, challenges-such as data imbalance and high-dimensional feature spaces-and emerging trends, including hybrid approaches and AI-enabled prognostic systems. This review aims to provide researchers and practitioners with an awareness of the strengths and compromises of various PdM methods and to help identify future research and build more robust, directed adaptive maintenance systems. Future work may include a systematic review of practical aspects such as public datasets, benchmarking platforms, and open-source tools to support the advancement of PdM research.