EDITOR ’ S CHOICE A GOLDEN OPPORTUNITY
ABB PROCESS AUTOMATION
Opportunities for Data Convergence and Analysis in Industrial Plants
By Vinod Ninan
Today , the average industrial plant uses less than 27 % of the data it generates , according to industry experts at the ARC Advisory Group , Boston ( arcweb . com ). Typically , the remaining 73 % of data — much of it produced by plant process-control system as highfrequency operational control ( OT ) data — is put in a historian and seldom used .
In addition , there are large volumes of other valuable functional data residing in the company ’ s general business or IT systems , and still more in the engineering systems ( ET ), covering specific design information for various assets . In addition to being rarely used , all of this data is normally scattered about in separate silos and networks that support little or no cross-referencing .
That ’ s where the golden opportunity lies , which we can now unlock with new software platforms that simplify better convergence and analysis of OT / IT / ET data . The benefits can be impressive , such as higher production rates from existing assets , less downtime as a result of predictivemaintenance practices , safer operation , reduced energy and other raw material inputs , and lower environmental impact .
By better convergence of OT / IT / ET data , we mean bringing together previously separate elements , which have now been streamlined and integrated so that they proceed seamlessly . To achieve this , we accumulate all OT , IT , and ET data in a data lake . Next , we contextualize and store related data in an industry-specific data model , such as paper making or plastic extruding , for example . Then we apply advanced analytics and industrial AI algorithms to identify correlations that were not previously visible .
Industrial artificial intelligence ( AI ) can play a major role in identifying these patterns and making process predictions . The terms AI ( artificial intelligence ) and ML ( machine learning ) are often used interchangeably , which can be confusing at times . AI is the overarching science of making machines and physical systems smarter by embedding “ artificial intelligence ” in them . ML is a subset of AI that involves systems gaining knowledge over time through “ self-learning ” to become smarter and more predictable , without human intervention .
As an example , consider a motor , which is an essential and omnipresent manufacturing asset in your plant . The motor generates a lot of operational data , such as temperature , pressure , and flow rate measurements from various stages of the production process .
In terms of the different types of data we have :
OT data : Motor speed , vibration level , and bearing temperature are typical parameters monitored in real time by OT systems to tell us how the motor is performing . This normally comes from automation system or a process controller components such as PLCs or a DCS .
IT data : If we want to see things such as the motor ’ s maintenance history , when it was last serviced , how much has been spent on repairs , or if the right bearings are in stock , we must find it in various IT systems , usually somewhere in the ERP solution .
ET data : Information about factors such as whether the motor is within its design speed limits , how much vibration it can take , the safe operating temperature for its bearings
6 PECM Issue 61