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

TL;DR

This paper presents a scalable, real-time predictive maintenance system for wind turbines using Big Data frameworks, enabling failure prediction, visualization, and fault tolerance in a cloud environment.

Contribution

It introduces an improved, automated Big Data-based predictive maintenance solution with enhanced speed, scalability, and fault tolerance for wind turbine failure prediction.

Findings

Enhanced data processing speed and scalability.

Fault-tolerant system with centralized monitoring.

Reduced operation and maintenance costs.

Abstract

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.