Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning

TL;DR

This paper introduces a privacy-preserving federated learning approach for wind turbine data, enabling fleet-wide model sharing without data transfer, improving fault detection accuracy, especially for turbines with limited local data.

Contribution

The paper presents a novel federated learning method tailored for wind turbines, maintaining data privacy while enhancing fleet-wide fault detection models.

Findings

Federated learning improves fault detection accuracy across turbines.

No turbine performance degrades due to federated participation.

Training times increase tenfold due to communication overhead.

Abstract

Terabytes of data are collected by wind turbine manufacturers from their fleets every day. And yet, a lack of data access and sharing impedes exploiting the full potential of the data. We present a distributed machine learning approach that preserves the data privacy by leaving the data on the wind turbines while still enabling fleet-wide learning on those local data. We show that through federated fleet-wide learning, turbines with little or no representative training data can benefit from more accurate normal behavior models. Customizing the global federated model to individual turbines yields the highest fault detection accuracy in cases where the monitored target variable is distributed heterogeneously across the fleet. We demonstrate this for bearing temperatures, a target variable whose normal behavior can vary widely depending on the turbine. We show that no turbine experiences a loss in model performance from participating in the federated learning process, resulting in superior performance of the federated learning strategy in our case studies. The distributed learning increases the normal behavior model training times by about a factor of ten due to increased communication overhead and slower model convergence.