A Deep Learning Framework for Wind Turbine Repair Action Prediction Using Alarm Sequences and Long Short Term Memory Algorithms

TL;DR

This paper presents a deep learning framework using LSTM models to predict repair actions from alarm sequences in offshore wind turbines, aiming to improve maintenance efficiency and reduce operational costs.

Contribution

It introduces a novel alarm sequence modeling approach with LSTM and biLSTM, demonstrating promising accuracy for predicting repair actions in wind turbine maintenance.

Findings

biLSTM achieved up to 76.01% test accuracy

The approach reduces false alarms and unnecessary vessel transfers

Potential for integration into existing O&M procedures

Abstract

With an increasing emphasis on driving down the costs of Operations and Maintenance (O&M) in the Offshore Wind (OSW) sector, comes the requirement to explore new methodology and applications of Deep Learning (DL) to the domain. Condition-based monitoring (CBM) has been at the forefront of recent research developing alarm-based systems and data-driven decision making. This paper provides a brief insight into the research being conducted in this area, with a specific focus on alarm sequence modelling and the associated challenges faced in its implementation. The paper proposes a novel idea to predict a set of relevant repair actions from an input sequence of alarm sequences, comparing Long Short-term Memory (LSTM) and Bidirectional LSTM (biLSTM) models. Achieving training accuracy results of up to 80.23%, and test accuracy results of up to 76.01% with biLSTM gives a strong indication to the potential benefits of the proposed approach that can be furthered in future research. The paper introduces a framework that integrates the proposed approach into O$\&$M procedures and discusses the potential benefits which include the reduction of a confusing plethora of alarms, as well as unnecessary vessel transfers to the turbines for fault diagnosis and correction.