Safety Monitoring for Large Language Models: A Case Study of Offshore Wind Maintenance

It has been forecasted that a quarter of the world's energy usage will be supplied from Offshore Wind (OSW) by 2050 (Smith 2023). Given that up to one third of Levelised Cost of Energy (LCOE) arises from Operations and Maintenance (O&M), the motive for cost reduction is enormous. In typical OSW farms hundreds of alarms occur within a single day, making manual O&M planning without automated systems costly and difficult. Increased pressure to ensure safety and high reliability in progressively harsher environments motivates the exploration of Artificial Intelligence (AI) and Machine Learning (ML) systems as aids to the task. We recently introduced a specialised conversational agent trained to interpret alarm sequences from Supervisory Control and Data Acquisition (SCADA) and recommend comprehensible repair actions (Walker et al. 2023). Building on recent advancements on Large Language Models (LLMs), we expand on this earlier work, fine tuning LLAMA (Touvron 2018), using available maintenance records from EDF Energy. An issue presented by LLMs is the risk of responses containing unsafe actions, or irrelevant hallucinated procedures. This paper proposes a novel framework for safety monitoring of OSW, combining previous work with additional safety layers. Generated responses of this agent are being filtered to prevent raw responses endangering personnel and the environment. The algorithm represents such responses in embedding space to quantify dissimilarity to pre-defined unsafe concepts using the Empirical Cumulative Distribution Function (ECDF). A second layer identifies hallucination in responses by exploiting probability distributions to analyse against stochastically generated sentences. Combining these layers, the approach finetunes individual safety thresholds based on categorised concepts, providing a unique safety filter. The proposed framework has potential to utilise the O&M planning for OSW farms using state-of-the-art LLMs as well as equipping them with safety monitoring that can increase technology acceptance within the industry

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

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