Amount Awarded: $20,000
This research centers on the standardization of implementation and the requirements of data interpretation systems for unmanned autonomous systems (UAS) in the offshore oil and gas industry with a focus on autonomous underwater pipeline inspections. The main objective of this Ph.D. research is to develop a framework, Warning Identification Framework (WIF), that captures the vulnerabilities of the data interpretation system through applied safety engineering and data science. The vulnerability identification and prioritization framework of anomalous signals within the data interpretation UAS is intended to capture the accident initiating events and safety barrier failures, enhance the decision-making process of an autonomous system, and allow for proactive orchestration of events during the remote operation.