SOFTWARE UPDATE A conversation with Senior Editor Patrick Waurzyniak

Chad Stoecker

Leader, Managed Services GE Digital, IPRC

Data-driven predictive analytics can transform plant engineering

How important is machine monitoring/plant diagnos- tics and the Industrial Internet of Things [IIoT] for manu- facturing productivity? It used to be enough to rely on alarms and trips to start

the maintenance and troubleshooting process in a plant. This is a reactive process that results in production losses and expenditure of unnecessary maintenance dollars. Today, most industries want more efficient solutions. Plant personnel can use data to determine when equipment problems start, instead of waiting until the asset is offline or has approached an alarm limit. Acting early will help companies have the time to make better economic decisions. Data analytics can transform the

plant from end to end. All areas of the plant are going to be data driven. Maintenance and reliability engineers can determine what maintenance to take and what maintenance actions can be avoided. Process and opera- tions personnel can use data to determine the most effi- cient way to run the plant and what activities are costing the company money. Business executives can use data to make better decisions related to the demands they get from the market.

What types of predictive analytics is GE Digital’s

Industrial Performance and Reliability Center using for improving machine reliability, uptime, etc.? Our solutions leverage the latest in cutting edge data

analytics techniques, and we support our solutions with industry experts with decades of reliability engineering expertise. GE’s digital industrial software has decades of expertise built into the software from industry subject mat- ter experts and data scientists. This expertise enables plant engineers to take advantage of GE’s collective experience with equipment to make better decisions, without having to be a data scientist.

What specific software applications are associated with IPRC’s analytics efforts, and how are they used? Asset performance management (APM) software, like

the Predix-based solution GE offers, have end-to-end capabilities that let companies switch more easily to a


more data-driven program by focusing on their particular problem area.

It is important to recognize that companies can get a

lot of value from their existing sensors by using predictive analytics technologies. Companies can increase uptime and reliability with sensors they have installed. Also, pre- dictive analytics solutions will recommend which sensors to prioritize for maintenance during an outage or which to prioritize for retrofit to existing equipment. Industrial software solutions can analyze and priori-

tize data to let companies make better use of the sensors that they already have installed. These solutions can then

A lot of people with decades of experience, aka ‘ma- chine whisperers,’ are leaving industry. This talent is being replaced by data.

prescribe what instrumentation will drive the most value in that environment if they could be added to the system. Moving forward, every new piece of equipment will have more instrumentation and data streaming off of it than the equipment it replaced.

How is this type of predictive analytics employing

what’s been called the ‘machine whisperer’ in helping to improve plant performance? A lot of people with decades of experience in machine

maintenance—we sometimes call them “machine whisper- ers”—are leaving industry. Their expertise is almost impos- sible to replace: They knew what was wrong just by touch and feel. This talent is being replaced by data. Our goal is to offer the next-generation workforce a

combination of people, processes, and technology built into software solutions that help them to make good decisions. Predictive analytics give plant engineers the right infor-

mation at the right time. Early warning gives plant engi- neers time to evaluate risk and create better maintenance schedules. This lets firms realize less downtime due to surprises and better-planned, shorter outages. Predictive analytics can allow for better management of spare parts and less waste due to unnecessary maintenance actions.

Fall 2016

Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68