Machine vision safeguard assurance

Abstract

AbstractWith the drive for increasing automation, unstaffed facilities, and improved process safety performance, new technologies are needed to assure that safe operations are being maintained. While typical practice has been to include increasingly complex sensors, they face limitations, especially at the human–machine interface. The transitory nature of high‐risk work and the difficulty with adding sensors to workers can leave this critical work as a blind spot to engineered systems. As such, we often rely on administrative or procedural controls to ensure that safeguards are in place and work remains safe. With the advancements in artificial intelligence, objects and actions can be recognized and monitored by a simple camera. With sufficient visual data, a camera can be trained to perform observations currently done by humans. This frees humans from routine tasks to do more abstract work and reduces or eliminates human failure modes that can weaken a safety system. This paper will highlight the work being done by Chevron to implement machine vision. Several use cases across the oil and gas value chain will be provided, showcasing their potential to improve process safety performance. Lessons learned from deployment and future growth of the technology will also be discussed.