ReviewSensors (Basel, Switzerland)2021
A Survey on Data-Driven Predictive Maintenance for the Railway Industry.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Vanilla LSTM Predictive Maintenance Model for Scientific Research Facilities.Sensors (Basel, Switzerland) · 2026Article
- A Survey of AI-Enabled Predictive Maintenance for Railway Infrastructure: Models, Data Sources, and Research Challenges.Sensors (Basel, Switzerland) · 2026Review
- Railway curve squeal prediction using environmental variables.Frontiers in artificial intelligence · 2026Article
- An explainable machine learning framework for railway predictive maintenance using data streams from the metro operator of Portugal.Scientific reports · 2025Article
- An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems.Sensors (Basel, Switzerland) · 2023Article
- Highly Reliable Multicomponent MEMS Sensor for Predictive Maintenance Management of Rolling Bearings.Micromachines · 2023Article
- The MetroPT dataset for predictive maintenance.Scientific data · 2022Article
- Prediction Models for Railway Track Geometry Degradation Using Machine Learning Methods: A Review.Sensors (Basel, Switzerland) · 2022Review
- Sensor-Based Predictive Maintenance with Reduction of False Alarms-A Case Study in Heavy Industry.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events-anomaly detection in time-series-can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.