# Real-time predictive maintenance for wind turbines using Big Data frameworks

arXiv: https://arxiv.org/abs/1709.07250 · HTML: https://mlsanity.com/paper/1709.07250/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2017.

## Built on by (most cited first, 7 of 7)

- [Scientometric Review of Artificial Intelligence for Operations & Maintenance of Wind Turbines: The Past, Present and Future](https://mlsanity.com/paper/2204.02360/) (2022)
- [Physically Meaningful Uncertainty Quantification in Probabilistic Wind Turbine Power Curve Models as a Damage Sensitive Feature](https://mlsanity.com/paper/2209.15579/) (2022)
- [Predicting machine failures from multivariate time series: an industrial case study](https://mlsanity.com/paper/2402.17804/) (2024)
- [Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification](https://mlsanity.com/paper/2307.14675/) (2023)
- [Predictive Maintenance Tool for Non-Intrusive Inspection Systems](https://mlsanity.com/paper/2103.01044/) (2021)
- [Predictive Maintenance -- Bridging Artificial Intelligence and IoT](https://mlsanity.com/paper/2103.11148/) (2021)
- [Real-Time AI-Driven Milling Digital Twin Towards Extreme Low-Latency](https://mlsanity.com/paper/2512.13482/) (2025)

## Abstract (arXiv)

This work presents the evolution of a solution for predictive maintenance to a Big Data environment. The proposed adaptation aims for predicting failures on wind turbines using a data-driven solution deployed in the cloud and which is composed by three main modules. (i) A predictive model generator which generates predictive models for each monitored wind turbine by means of Random Forest algorithm. (ii) A monitoring agent that makes predictions every 10 minutes about failures in wind turbines during the next hour. Finally, (iii) a dashboard where given predictions can be visualized. To implement the solution Apache Spark, Apache Kafka, Apache Mesos and HDFS have been used. Therefore, we have improved the previous work in terms of data process speed, scalability and automation. In addition, we have provided fault-tolerant functionality with a centralized access point from where the status of all the wind turbines of a company localized all over the world can be monitored, reducing O&M costs.