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

• URL: http://arxiv.org/abs/2207.09457v1
• Date: Tue, 19 Jul 2022 12:11:06 GMT
• Title: A Deep Learning Framework for Wind Turbine Repair Action Prediction Using Alarm Sequences and Long Short Term Memory Algorithms
• Authors: Connor Walker, Callum Rothon, Koorosh Aslansefat, Yiannis Papadopoulos, Nina Dethlefs
• Abstract summary: Condition-based monitoring (CBM) has been at the forefront of recent research developing alarm-based systems and data-driven decision making. 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 models.
• Score: 1.2599533416395765
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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