Mary Ann Lundteigen

Modeling degradation processes in subsea systems is important for assessing system reliability, but it remains challenging due to the stochastic nature of degradation and the limited availability of degradation data. Existing approaches commonly assume a specific stochastic degradation process and subsequently estimate its parameters from observed degradation data. This paper proposes an augmented adaptive interacting multi-model sequential Monte Carlo (AA-IMM-SMC) framework for the joint identification of stochastic degradation processes and parameter estimation, with a focus on time-based stochastic processes. The proposed framework utilizes a predefined library of candidate degradation models and augments the propagation model to enable simultaneous degradation state and parameter estimation. Adaptive resampling with particle resource allocation, parameter perturbation, probability-weighted model interaction, and a persistence-based model selection criterion are incorporated to improve estimation and model identification under stochastic degradation behavior. Evaluation is conducted through numerical simulations, including sensitivity and robustness analyses, and two real-world subsea choke valve degradation datasets. The proposed framework provides a sequential approach for degradation model discovery that can reduce reliance on a single assumed degradation process.

The IEC 62443 standard defines that, based on risk assessment, different parts of an Industrial Automation and Control System (IACS) may have different security levels, and that parts with the same security level can be designated as separate zones. Furthermore, communication between different zones, both intra-IACS and inter-IACS, can be done via conduits. In this article, we argue that zones and particularly conduits can benefit from more detailed discussions of their architecture and implementation. Consequently, as novel contributions we (1) describe detailed principles for implementing conduits; (2) outline a process for connecting zones with potentially different Security Levels (SLs), expressed in the form of a flow chart; and (3) discuss challenges related to the application of zones and conduits in practice.

As one of the most important assets in the transportation of oil and gas products, subsea pipelines are susceptible to various environmental hazards, such as mechanical damage and corrosion, that can compromise their structural integrity and cause catastrophic environmental and financial damage. Autonomous underwater systems (AUS) are expected to assist offshore operations personnel and contribute to subsea pipeline inspection, maintenance, and damage detection tasks. Despite the promise of increased safety, AUS technology needs to mature, especially for image-based inspections with computer vision methods that analyze incoming images and detect potential pipeline damage through anomaly detection. Recent research addresses some of the most significant computer vision challenges for subsea environments, including visibility, color, and shape reconstruction. However, despite the high quality of subsea images, the lack of training data for reliable image analysis and the difficulty of incorporating risk-based knowledge into existing approaches continue to be significant obstacles. In this paper, we analyze industry-provided images of subsea pipelines and propose a methodology to address the challenges faced by popular computer vision methods. We focus on the difficulty posed by a lack of training data and the opportunities of creating synthetic data using risk analysis insights. We gather information on subsea pipeline anomalies, evaluate the general computer vision approaches, and generate synthetic data to compensate for the challenges that result from lacking training data, and evidence of pipeline damage in data, thereby increasing the likelihood of a more reliable AUS subsea pipeline inspection for damage detection.

Cyber-physical systems are taking on a permanent role in the industry, such as in oil and gas or mining. These systems are expected to perform increasingly autonomous tasks in complex settings removing human operators from remote and potentially hazardous environments. High autonomy necessitates a more extensive use of artificial intelligence methods, such as anomaly detection, to identify unusual occurrences in the monitored environment. The absence of data characterizing potentially hazardous events leads to disruptive noise displayed as false alarms, a common anomaly detection issue for hazard identification applications. Contrastingly, disregarding the false alarms can result in the opposite effect, causing loss of early indications of hazardous occurrences. Existing research introduces simulating and extrapolating less represented data to expand the information on hazards and semi-supervise the methods or by introducing thresholds and rule-based methods to balance noise and meaningful information, necessitating intensive computing resources. This research proposes a novel Warning Identification Framework that evaluates risk analysis objectives and applies them to discern between true and false warnings identified by anomaly detection. We demonstrate the results by analyzing three seismic hazard assessment methods for identifying seismic tremors and comparing the outcomes to anomalies found using the unsupervised anomaly detection method. The demonstrated approach shows great potential in enhancing the reliability and transparency of anomaly detection outcomes and, thus, supporting the operational decision-making process of a cyber-physical system.