Asset intensive industries own and operate many mechanical, electrical, and structural assets. These include power generation, power distribution network switches, transformers, metering devices, gas compressors, fluid pumps, cooling fans, heat exchangers, extruders, valving, piping, and many other assets. These assets work together as systems to produce product.
Historically, these assets are monitored by supervisory systems with supplemental human based inspections. Any asset failure detection is seen in control systems parameters such as power metering, temperature, pressure, flow, and of course by customer outage reports. Human based “route” inspections offer more diagnostic information to predict future maintenance or operational adjustments. However, route data is rarely corelated with operational context nor performed frequently enough for timely adjustment planning. This historic perspective leaves companies in a reactive or firefighting more.
With the advent of low cost industrial IoT data collection tools, data collection becomes available daily, and is available to merge with operational data. With synchronized asset monitoring, operational data, and advanced analytics, predictions of future maintenance and operational adjustments are made. This improves maintenance efficiency, increases uptime and output, and lowers operational costs.
To reach the objective of monitoring equipment and integration with maintenance and operations applications, an ecosystem of IT and OT technologies and vendors is often the norm. This presentation describes the asset and process modeling process, tools for remotely monitoring the assets, and example analytics for improving maintenance and operations activities.