In the last few years, the cost of sensors and of data storage and computation is strongly reduced, fostering the concept known as Internet of Things (IoT) which takes advantage of pervasive collection of data to analyze the operating parameters of critical and not-critical assets. The goal is to improve the human machines interaction and to improve the industrial productivity and efficiency.
This presentation presents a solution developed for monitoring large fleets of rotating machines, such as gas compressors, pumps, turbines, electrical motors and generators of different power ranges distributed over multiple sites.
The solution gathers multiple data sources (vibrations, process, electrical and control signals) to define the health status and the residual lifetime of the equipment, information used to define when and how to perform predictive maintenance actions and to optimize the operation of the machine by improving its uptime and efficiency.
The information coming from different sites can be integrated to monitor all sites from a single maintenance or reliability center by using an external or internal cloud infrastructure: this solution allows to compare the performance of similar assets in different sites and to perform statistical analysis of their operation and failure types and rates.
A reference case is presented, where this solution provides significant improvements in the management of the rotating assets in an important European chemical site.
ABB, Diego Pareschi