Leveraging the IIoT for Asset Performance Management (APM)

How well is our equipment performing?  Are we getting the best mileage from it?  When will machine/pump/motor X fail?  What can we do better?  How can we even find out?  These are the kinds of questions that keep production managers awake at night, and that may eventually lead them to consider implementing Asset Performance Management.

The term “Asset Performance Management” or APM, has been broadly defined by MESA International as “an approach to managing the optimal deployment of assets to maximize profitability and predictability in product supply.”  In other words, APM means making sure your equipment works, and works well.

The need for APM is clear.  Asset failure costs millions of dollars in lost production each year.  But that’s not all.  Workers who lack of critical data about the state of their equipment can make expensive mistakes.  Broken or poorly-functioning machinery can cause accidents.  Failure to manage assets properly will eventually lead to higher insurance costs.

A New Approach to APM

Until recently, APM has been used mainly for high-cost, mission-critical machinery.  Data collection was based on scheduled manual readings of sensors mounted on machines, a costly, time-consuming effort.  Performance stats for less expensive assets were typically derived from plant walk-throughs, operator experience, and educated guesses.  In some cases it was more cost effective to simply allow equipment to run until failure, and then replace it.

The advent of the IIoT is changing all of that.  As the cost of sensors drop, as wireless technologies improve, and as Big Data systems come online, it is now becoming cost-effective to monitor more and smaller assets, and do it in real time.  Doing APM in real time allows managers to run advanced analytics on the data, and do predictive maintenance.  Now, instead of shutting down a process to replace a burnt-out fan motor, or guessing when the motor might need to be replaced, an engineer can run the motor until just before failure, and then switch it out at the optimal time.

Early Adopters

Commenting about APM, Rich Carpenter, the Chief Technology Strategist for GE Intelligence Platforms, said, “The purpose of running advanced analytics is to have early detection of problems, so that we can prevent in real time, rather than react in real time.”  GE follows a simple, four-step process for IoT-based APM:

  1. Connect to sensors on the plant equipment or for the control system.
  2. Send the sensor data via DMZ or other protected environment to the cloud.
  3. Organize the data, according to type of customer, site, machine, etc.
  4. Run advanced analytics to manage the assets.

Hartford Steam Boiler (HSB), an industrial insurance company, has a vision that IoT-powered APM may transform their business into becoming an industrial service provider.  Instead of simply insuring against mechanical failure, by putting IoT-connected sensors on their customers equipment, the company can check performance, find the risk of breakdown, and recommend timely replacement or repair.  “The Internet of Things is the next industrial revolution and we have to position ourselves,” said Greg Barats, CEO of HSB.  “IoT start-ups are a fantastic way of jump-starting your thinking.”

What visionaries like GE and HSB have in common is an understanding of the potential of IIoT to be a game-changer for APM.  We share that vision.  In fact, we see APM as a logical application for IIoT technology, and supply the necessary software and services to securely access the necessary data in real time, to make it happen.