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

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.

SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0

“…Some studies refer both on CM data and event data. For example, Canizo et al [28] presented a Big Data analytics approach for the renewable energy field. In particular, they developed a PdM application for wind turbines by using a Big Data processing framework to generate data-driven predictive models that are based upon historical operational data (e.g., power, wind speed, rotor speed, and generator speed recorded) and system status data, previously stored in the cloud.…”

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