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

Walker, Connor; Rothon, Callum; Aslansefat, Koorosh; Papadopoulos, Yiannis; Dethlefs, Nina

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.

Citation

Walker, C., Rothon, C., Aslansefat, K., Papadopoulos, Y., & Dethlefs, N. (2022). A Deep Learning Framework for Wind Turbine Repair Action Prediction Using Alarm Sequences and Long Short Term Memory Algorithms. Lecture notes in computer science, 13525 LNCS, 189-203. https://doi.org/10.1007/978-3-031-15842-1_14

• Journal Article Type: Article
• Acceptance Date: Mar 15, 2022
• Online Publication Date: Sep 9, 2022
• Publication Date: Jan 1, 2022
• Deposit Date: Jun 20, 2024
• Publicly Available Date: Jul 22, 2022
• Print ISSN: 0302-9743
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 13525 LNCS
• Pages: 189-203
• DOI: https://doi.org/10.1007/978-3-031-15842-1_14
• Keywords: Learning; Artificial Intelligence
• Public URL: https://hull-repository.worktribe.com/output/4033230