# Data-driven online monitoring of wind turbines

## TL;DR

This paper introduces a statistical, data-driven approach for online wind turbine monitoring that uses adaptive thresholds to predict failures earlier than existing methods, enhancing condition-based maintenance.

## Contribution

It presents a novel adaptive alarm threshold method for wind turbine condition monitoring, integrating sensor data to improve early failure detection.

## Key findings

- Earlier failure prediction compared to traditional methods
- Effective integration of multiple sensor data sources
- Adaptive thresholds improve warning accuracy

## Abstract

Condition based maintenance is a modern approach to maintenance which has been successfully used in several industrial sectors. In this paper we present a concrete statistical approach to condition based maintenance for wind turbine by applying ideas from statistical process control. A specific problem in wind turbine maintenance is that failures of a certain part may have causes that originate in other parts a long time ago. This calls for methods that can produce timely warnings by combining sensor data from different sources. Our method improves on existing methods used in wind turbine maintenance by using adaptive alarm thresholds for the monitored parameters that correct for values of other relevant parameters. We illustrate our method with a case study that shows that our method is able to predict upcoming failures much earlier than currently used methods.