# Boosting-inspired online learning with transfer for railway maintenance

arXiv: https://arxiv.org/abs/2504.08554 · HTML: https://mlsanity.com/paper/2504.08554/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 70 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Builds on (most cited first, 26 of 26)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Lifelong Learning with Dynamically Expandable Networks](https://mlsanity.com/paper/1708.01547/) (2017)
- [Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs](https://mlsanity.com/paper/1802.10026/) (2018)
- [Deep Ensembles: A Loss Landscape Perspective](https://mlsanity.com/paper/1912.02757/) (2019)
- [Expert Gate: Lifelong Learning with a Network of Experts](https://mlsanity.com/paper/1611.06194/) (2016)
- [BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning](https://mlsanity.com/paper/2002.06715/) (2020)
- [REMIND Your Neural Network to Prevent Catastrophic Forgetting](https://mlsanity.com/paper/1910.02509/) (2019)
- [On the Theory of Transfer Learning: The Importance of Task Diversity](https://mlsanity.com/paper/2006.11650/) (2020)
- [Training independent subnetworks for robust prediction](https://mlsanity.com/paper/2010.06610/) (2020)
- [Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams](https://mlsanity.com/paper/2009.00919/) (2020)
- [Learning to cluster in order to transfer across domains and tasks](https://mlsanity.com/paper/1711.10125/) (2017)
- [Conditional Channel Gated Networks for Task-Aware Continual Learning](https://mlsanity.com/paper/2004.00070/) (2020)
- [Optimal Continual Learning has Perfect Memory and is NP-hard](https://mlsanity.com/paper/2006.05188/) (2020)
- [Learning Neural Network Subspaces](https://mlsanity.com/paper/2102.10472/) (2021)
- [Model Zoo: A Growing "Brain" That Learns Continually](https://mlsanity.com/paper/2106.03027/) (2021)
- [CoSCL: Cooperation of Small Continual Learners is Stronger than a Big One](https://mlsanity.com/paper/2207.06543/) (2022)
- [Divide and not forget: Ensemble of selectively trained experts in Continual Learning](https://mlsanity.com/paper/2401.10191/) (2024)
- [Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing](https://mlsanity.com/paper/2101.00509/) (2021)
- [Continual Learning Beyond a Single Model](https://mlsanity.com/paper/2202.09826/) (2022)

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

- [Bridging Streaming Continual Learning via In-Context Large Tabular Models](https://mlsanity.com/paper/2512.11668/) (2025)
- [Collaborative Streaming Anomaly Detection with Interactive Explanations and Ensemble Consensus](https://mlsanity.com/paper/2609.23883/) (2026)

## Abstract (arXiv)

The integration of advanced sensor technologies with deep learning algorithms has revolutionized fault diagnosis in railway systems, particularly at the wheel-track interface. Although numerous models have been proposed to detect irregularities such as wheel out-of-roundness, they often fall short in real-world applications due to the dynamic and nonstationary nature of railway operations. This paper introduces BOLT-RM (Boosting-inspired Online Learning with Transfer for Railway Maintenance), a model designed to address these challenges using continual learning for predictive maintenance. By allowing the model to continuously learn and adapt as new data become available, BOLT-RM overcomes the issue of catastrophic forgetting that often plagues traditional models. It retains past knowledge while improving predictive accuracy with each new learning episode, using a boosting-like knowledge sharing mechanism to adapt to evolving operational conditions such as changes in speed, load, and track irregularities. The methodology is validated through comprehensive multi-domain simulations of train-track dynamic interactions, which capture realistic railway operating conditions. The proposed BOLT-RM model demonstrates significant improvements in identifying wheel anomalies, establishing a reliable sequence for maintenance interventions.