# Scalable, Technology-Agnostic Diagnosis and Predictive Maintenance for Point Machine using Deep Learning

arXiv: https://arxiv.org/abs/2508.11692 · HTML: https://mlsanity.com/paper/2508.11692/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 1 of 1)

- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)

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

- [The Blueprints of Intelligence: A Functional-Topological Foundation for Perception and Representation](https://mlsanity.com/paper/2512.05089/) (2025)
- [Building Trust in Artificial Intelligence: A Necessity for Railway Applications](https://mlsanity.com/paper/2609.18278/) (2026)

## Abstract (arXiv)

The Point Machine (PM) is a critical piece of railway equipment that switches train routes by diverting tracks through a switchblade. As with any critical safety equipment, a failure will halt operations leading to service disruptions; therefore, pre-emptive maintenance may avoid unnecessary interruptions by detecting anomalies before they become failures. Previous work relies on several inputs and crafting custom features by segmenting the signal. This not only adds additional requirements for data collection and processing, but it is also specific to the PM technology, the installed locations and operational conditions limiting scalability. Based on the available maintenance records, the main failure causes for PM are obstacles, friction, power source issues and misalignment. Those failures affect the energy consumption pattern of PMs, altering the usual (or healthy) shape of the power signal during the PM movement. In contrast to the current state-of-the-art, our method requires only one input. We apply a deep learning model to the power signal pattern to classify if the PM is nominal or associated with any failure type, achieving >99.99% precision, <0.01% false positives and negligible false negatives. Our methodology is generic and technology-agnostic, proven to be scalable on several electromechanical PM types deployed in both real-world and test bench environments. Finally, by using conformal prediction the maintainer gets a clear indication of the certainty of the system outputs, adding a confidence layer to operations and making the method compliant with the ISO-17359 standard.