This paper reviews the recent literature on machine learning (ML) models that have been used for condition monitoring in wind turbines (e.g. blade fault detection or generator temperature monitoring). We classify these models by typical ML steps, including data sources, feature selection and extraction, model selection (classification, regression), validation and decision-making. Our findings show that most models use SCADA or simulated data, with almost two-thirds of methods using classification and the rest relying on regression. Neural networks, support vector machines and decision trees are most commonly used. We conclude with a discussion of the main areas for future work in this domain.@en

In future inspections of offshore assets utilizing robots, robots will not only be expected to collate new data from their payload of instruments, but they will also be expected to interact with the infrastructure being inspected, undertake remedial tasks and engage with embedded monitoring systems of the asset. This increasing level of interaction and deployment frequency of robot inspections requires an understanding of how we can embed safe and trusted operational architectures within robots. Currently, robots can undertake constrained semi-autonomous inspections, using predetermined tasks (missions) with minimum supervision. However, the challenge is that the state of the world changes with time as does the condition of the robot. Therefore, robots must be able to undertake adaptive measures to support optimal outcomes during autonomous missions. In this paper, we propose an initial architecture to the safe verification and validation of health condition and certification of robotic and autonomous inspection systems for offshore assets. Our first contribution relates to the verification and validation architecture, which takes into account risks associated with asset inspection, safety protocols, evolving ambient changes, as well as the inherent state of health of the robot. The second part of our paper looks to how prognostic analytics can be used to support robot resilience in terms of sensor drift and accurate state of health estimates of critical sub-systems. Initial results demonstrate that methods such as relevance vector machines and Bayesian networks can be used to accurately mitigate risks to autonomy.@en

In this paper, failure analysis of an electromagnetic relay is studied to inform embedded health monitoring for the prediction of Remaining Useful Life (RUL) of electromagnetic relays. A statistical design of experiment with online monitoring of voltage, current, temperature, resistance, duty cycle, and frequency is used to identify relay failure precursors. Examples of detected precursors include abnormal spikes in resistance and extension of the average closing time of the relay within the final 1.5 million actuations prior to failure. A detailed post-failure analysis utilising white light interferometry and energy dispersive X-ray (EDX) analysis reveals the relay failure modes associated with erosion, pit and crater formation, and material transfer. The results demonstrate that low resolution data can detect signatures of impending failure modes such as stuck open and closed failures as well as unacceptable long opening times. The findings within this paper demonstrate remote monitoring of the near real-time health of electromagnetic relays with low resolution data analysis.@en

Autonomous systems are increasingly being used in safety-and mission-critical domains, including aviation, manufacturing, healthcare and the automotive industry. Systems for such domains are often verified with respect to essential requirements set by a regulator, as part of a process called certification. In principle, autonomous systems can be deployed if they can be certified for use. However, certification is especially challenging as the condition of both the system and its environment will surely change, limiting the effective use of the system. In this paper we discuss the technological and regulatory background for such systems, and introduce an architectural framework that supports verifiably-correct dynamic self-certification by the system, potentially allowing deployed systems to operate more safely and effectively.@en

Subsea power cables are critical assets within the distribution and transmission infrastructure of electrical networks. Over the past two decades, the size of investments in subsea power cable installation projects has been growing significantly. However, the analysis of historical failure data shows that the present state-of-the-art monitoring technologies do not detect about 70% of the failure modes in subsea power cables. This paper presents a modeling methodology for predicting damage along the length of subsea cables due to environmental conditions (e.g., seabed roughness and tidal flows) which result in the loss of the protective layers on the cable due to corrosion and abrasion (accounting for over 40% of subsea cable failures). For a defined cable layout on different seabed conditions and tidal current inputs, the model calculates the cable movement by taking into account the scouring effect and then it predicts the rate at which the material is lost due to corrosion and abrasion. Our approach integrates accelerated aging data using a Taber test which provides abrasion wear coefficients for the cable materials. The models have been embedded into a software tool that predicts the life expectancy of the cable and demonstrated for narrow conditions, where the tidal flow is unidirectional and perpendicular to the power cable. The paper also provides discussion on how the developed models can be used with other condition monitoring data sets in a prognostics framework.@en

Robots are increasingly used to carry out critical missions in extreme environments that are hazardous for humans. This requires a high degree of operational autonomy under uncertain conditions, and poses new challenges for assuring the robot's safety and reliability. In this paper, we develop a framework for probabilistic model checking on a layered Markov model to verify the safety and reliability requirements of such robots, both at pre-mission stage and during runtime. Two novel estimators based on conservative Bayesian inference and imprecise probability model with sets of priors are introduced to learn the unknown transition parameters from operational data. We demonstrate our approach using data from a real-world deployment of unmanned underwater vehicles in extreme environments.@en