Abstract
Abstract Instabilities and failure in many industrial structures can be too costly. That includes the pipeline structures for oil and gas industries or power generation plans and infrastructural transit systems. Prognostics and health management, along with Preventive, predictive, and prescriptive maintenance, are alternative options to avoid the failure in these systems by smart and on-time maintenance. However, although it is possible to collect data dynamically from these systems through their service periods, in many cases, a trustworthy and reliable knowledge base to allow making the right decisions is not always available. This paper presents the concept of LIVE Digital Twin that relies on four phases of Learn, Identify, Verify, Extend employing various Computer-Aided Engineering (CAE) simulation strategies during the life span of the structure parallel to its design, performance, inspection, and maintenance. The architecture of LIVE Digital Twin is presented, and the details are described along with some practical case studies in Light Rail Transit (LRT) and pipeline systems in oil and gas industries. The presented concept and architecture of LIVE Digital Twin can be employed and implemented for various other applications and non-structural systems.
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