Analytics are one of the “killer apps” for edge computing due to their ability to deliver concrete business outcomes in areas such as reduced downtime, higher asset utilization, product quality, and insight into the state of assets and processes. This vision has extended into applications of artificial intelligence (AI), machine learning (ML), virtual reality (VR), augmented reality (AR), machine vision, and video analytics. Automation end users employ these technologies for both traditional performance goals and in new use cases like mobility, autonomy, enhanced field operations, connected remote experts, personnel tracking, safety, logistics, and supply chain.
The oil and gas industry has been heavily data-driven for years but with the advent of edge computing, it now can deploy AI into the field and perform data analysis on the spot, enabling greater autonomy and transforming day-to-day operations. This session will showcase an upstream oil industry application that deploys manageable intelligence into the field. In addition, a utility engineer presents the case of a multi-unit hydroelectric plant that used digitalization, integration, and modernization of its islanded end devices for improved station analytics.