ASSET ASSESSMENT METHOD IN A MV PREDICTIVE MODEL TO ESTIMATE THE ASSET STATUS

Abstract

Healthy equipment are vital for ensuring process uptime in oil & gas production and chemical plants. Advanced monitoring and diagnostic methods are commonly considered the good approach to provide relevant information for a successful condition-based and predictive maintenance.In the paper, a smart computational technique able to consider the impact of the environmental and operational conditions on the health index calculation will be proposed.The combination of this new method with the usage of innovative sensors (IoT) and digital systems allow to include the "real" operative conditions of the apparatus in the update of the health status and to provide more "realistic" Probability of Failure and the Residual Useful Life for the Medium Voltage equipment in Oil and Gas and chemical applications.The use of digital asset management models to monitor the asset status and the availability of data analytics methods, drives the asset maintenance strategy