Does IoT power predictive maintenance in the manufacturing industry?

In this article, Joseph Zulick, writer and manager at MRO Electric and Supply, discusses how IoT is changing predictive maintenance for the better…

For far too often maintenance has been treated as the proverbial red headed step child. Now with the internet of things (IoT), has brought new technology in a useful package to our factory maintenance.

New sensor technology can measure pressure, distance, temperature, and much more in a smaller package and from greater distances. No longer are you required to modify a machine or tool, you can now use external laser technology to measure quality, and keep track on machine maintenance. If you want to know if the machine is deviating outside of its temperature range, you’ll know. If you need to track power usage, you can see the read out from the beach in Florida.

Sensors can directly connect to a hub which transmits information to a program that compiles the data, compares it, then uploads the important results. Sensors can connect directly but that’s a lot of internet or intranet connections. Hubs compile and report on the devices.

Think about your home and your stove, an over boiling pot of macaroni you child started before taking that text message. imagine sensors determining spillage on your cook top and automatically shutting off the burner.

This is the first stage of Smart Technology, self-analysis. You could have a sensor connect to a monitor. That monitor checks for spillage and when it sees it, send you a text message. You then go online, check into your home network and turn off stove. This is informational and provides data and you have access to login and make changes, however, don’t you want the device to act on its own in this condition?

This is what we are talking about today. Closing the loop, open loop means you tell something to happen and it happens but you’re not sure it happened you have no feedback. Closed loop gets that feedback to confirm what you expected to happen, did happen. Smart Technology acts upon this information.

These loops are ever expanding. Closed loop at sensor level, closed loop at machine level, closed loop at network level. If something is going to be safety decision or crash related. This needs to be as instantaneous as possible. Lower priority decisions can wait for human intervention. Decision making if you will.

An important step in all of this technology is closing the loop. Let’s say a machine like a stamping press produces car parts. As the tool gets dull the tonnage goes up. At a predetermined limit the press will stop with a warning. It will also alert the tool room to do maintenance on the tool and the production team that they need to address the shortage of parts. All very important aspects.

Predictive maintenance though is the future and the now. Instead of just telling me after it happens (reactive), let’s say it informs me hours, days, weeks or even months in advance.

Unplanned downtime costs far more than planned maintenance. You have the cost of lost work, moving staff, priority shipments, panicked purchasing agents, all no longer doing what the were scheduled to do and now reacting to a problem.

Predictive maintenance looks at the predetermined expectations of failure and starts to learn on the fly. Using smart technology and sophisticated calculations you now are no longer locked into a planned maintenance, far before failure or a reactive downtime condition. Now machines and software link together to design advanced algorithms that can assist you with better information. Smarter machines, smarter calculations, smarter decisions.

Rarely in maintenance do you see a linear wear pattern. On mechanical brakes, hydraulic valves or electronic sensors, all have a curve that accelerated over time until failure. This unpredictable nature is what computer systems and Smart controls attempt to replicate.

Systems start of with linear parameters, time, distance measurements, temperature readings. Over a short period of time these systems start to educate themselves as predictable wear patterns start to occur. These systems take into account things like time to failure rates, temperature changes, and monitor the deviation patterns that are occurring. From this the machines, controls and data gathering provides the information. The computer modeling and software start to determine failure dates based on current information, past history and future planned production.

Imagine sitting at your desk and getting a list with the expected dates of failure for tooling, equipment, material and even getting these dates far enough that you can plan for long lead time items that can crush a manufacturing deadline.

Information is getting smarter, equipment and controls are getting smarter, computer systems are getting smarter, shouldn’t we get smarter? Now if only I could predict when Bobby the operator is going to take a long weekend.

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