Damage detection in operational wind turbine blades using a new approach based on machine learning

TL;DR

This paper introduces a novel machine learning-based structural health monitoring method using Gaussian Processes to detect early damage in wind turbine blades by analyzing their vibrational frequencies and comparing blades over time.

Contribution

The paper presents a new SHM approach leveraging Gaussian Processes to predict blade frequencies and detect damage early, improving damage detection in operational wind turbines.

Findings

Successfully identified damage up to six months early

Validated method on real wind turbine data

Achieved early damage detection with control chart analysis

Abstract

The application of reliable structural health monitoring (SHM) technologies to operational wind turbine blades is a challenging task, due to the uncertain nature of the environments they operate in. In this paper, a novel SHM methodology, which uses Gaussian Processes (GPs) is proposed. The methodology takes advantage of the fact that the blades on a turbine are nominally identical in structural properties and encounter the same environmental and operational variables (EOVs). The properties of interest are the first edgewise frequencies of the blades. The GPs are used to predict the edge frequencies of one blade given that of another, after these relationships between the pairs of blades have been learned when the blades are in a healthy state. In using this approach, the proposed SHM methodology is able to identify when the blades start behaving differently from one another over time. To validate the concept, the proposed SHM system is applied to real onshore wind turbine blade data, where some form of damage was known to have taken place. X-bar control chart analysis of the residual errors between the GP predictions and actual frequencies show that the system successfully identified early onset of damage as early as six months before it was identified and remedied.