Early warning method of unsafe behavior accidents for offshore drilling operators based on eye-tracking trajectory

Abstract

Despite the implementation of highly automated and intelligent equipment and facilities at the offshore drilling platform in recent years, drilling operators remain indispensable. During offshore drilling operations, the behavior of operators is susceptible to numerous factors including personal attributes, environmental interferences, and equipment feedback. When the factors that affect the operation of drilling change adversely, unsafe behavior of drilling operators can easily occur, resulting in offshore drilling accidents. As such, the early warning of unsafe behavior accidents of drilling operators is essential to reduce the occurrence of offshore drilling accidents. Firstly, attempts were made to use eye-tracking technology to record the eye movement data of unsafe behavior of drilling operators in real-time. In total, 42 drilling operation trainers had their eye-tracking trajectories recorded in real-time during normal tripping operations throughout the drilling process. A total of 252 eye-tracking trajectory data points were collected for six drilling behaviors, including normal operation, unskilled operation, fatigue operation, action errors, omitted actions and wrong order. Secondly, based on the IETTSM (Improved Eye-tracking Trajectory Sequence Method) algorithm, the eye-tracking trajectory of drilling operators was serialized. Thirdly, an evaluation model of drilling operator operation behavior was constructed based on the DLD (Damerau-Levenshtein Distance) algorithm. Fourthly, the IETTSM-DLD model was used to prompt abnormal behavior among drilling operators, which was accomplished by referencing the appropriate trajectory nodes and issuing early warnings of unsafe behavior based on the coupling of said trajectory nodes. The results reveal that the proposed IETTSM-DLD model can realize real-time prompting of abnormal operation of offshore drilling operators and early warning of accidents caused by unsafe behavior. The average early warning accuracy rate was found to be 91.9%, with an average early warning time of 120.97 s.