This research examines the factors contributing to the exterior material degradation of subsea oil and gas pipelines monitored with autonomous underwater systems (AUS). The AUS have a role of gathering image data that is further analyzed with artificial intelligence data analysis methods. Corrosion and potential ruptures on pipeline surfaces are complex processes involving several competing elements, such as the geographical properties, composition of soil, atmosphere, and marine life, whose effects can result in substantial environmental damage and financial loss. Despite extensive research, corrosion monitoring and prediction remain a persistent challenge in the industry. There is a lack of knowledge map that can enable image analysis using an AUS to recognize ongoing degradation processes and potentially prevent substantial damage. The main contribution of this research is the knowledge map for increased context and risk awareness to improve the reliability of image-based monitoring and inspection by autonomous underwater systems in detecting hazards and early signs of material degradation on subsea pipeline surfaces.

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.

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This research examines the factors contributing to the exterior material degradation of subsea oil and gas pipelines monitored with autonomous underwater systems (AUS). The AUS have a role of gathering image data that is further analyzed with artificial intelligence data analysis methods. Corrosion and potential ruptures on pipeline surfaces are complex processes involving several competing elements, such as the geographical properties, composition of soil, atmosphere, and marine life, whose eflt in substantial environmental damage and financial loss. Despite extensive research, corrosion monitoring and prediction remain a persistent challenge in the industry. There is a lack of knowledge map that can enable image ausing an AUS to recognize ongoing degradation processes and potentially prevent substantial damage. The main contribution of this research is the knowledge map for increased context and risk awareness to improve the reliability of image-based monitoring and inspection by autonomous underwater systems in detecting hazards and early signs of material degradation on subsea pipeline surfaces.