Vibration Fault Diagnosis in Wind Turbines based on Automated Feature Learning

TL;DR

This paper introduces a novel, scalable fault diagnosis method for wind turbine gearboxes using autonomous feature learning with neural networks, eliminating the need for human-defined fault signatures and enabling comprehensive spectral analysis.

Contribution

It presents a data-driven, autonomous fault diagnosis approach combining CNNs and isolation forests, overcoming limitations of traditional signature-based methods.

Findings

High accuracy in gearbox fault detection demonstrated

No need for gearbox-specific expertise or predefined spectral features

Effective monitoring across the full vibration spectrum

Abstract

A growing number of wind turbines are equipped with vibration measurement systems to enable a close monitoring and early detection of developing fault conditions. The vibration measurements are analyzed to continuously assess the component health and prevent failures that can result in downtimes. This study focuses on gearbox monitoring but is applicable also to other subsystems. The current state-of-the-art gearbox fault diagnosis algorithms rely on statistical or machine learning methods based on fault signatures that have been defined by human analysts. This has multiple disadvantages. Defining the fault signatures by human analysts is a time-intensive process that requires highly detailed knowledge of the gearbox composition. This effort needs to be repeated for every new turbine, so it does not scale well with the increasing number of monitored turbines, especially in fast growing portfolios. Moreover, fault signatures defined by human analysts can result in biased and imprecise decision boundaries that lead to imprecise and uncertain fault diagnosis decisions. We present a novel accurate fault diagnosis method for vibration-monitored wind turbine components that overcomes these disadvantages. Our approach combines autonomous data-driven learning of fault signatures and health state classification based on convolutional neural networks and isolation forests. We demonstrate its performance with vibration measurements from two wind turbine gearboxes. Unlike the state-of-the-art methods, our approach does not require gearbox-type specific diagnosis expertise and is not restricted to predefined frequencies or spectral ranges but can monitor the full spectrum at once.