IIoT Applied: Predictive Maintenance on a Compressor

March 5, 2018
Like most new projects or ideas, the key to success lies in getting started. With the Industrial Internet of Things, getting started is easier than you might think. This article highlights an example of how to begin.

You may have been thinking about doing an Internet of Things (IoT) application that could help your company, but embarking on your first industrial IoT application might seem daunting.

I’ve heard Benson Hougland, Opto 22’s vice president of marketing and product strategy, tell people to “start small” or “just get started.”

But how do you do that? My advice is to think of a desired outcome first and go backwards to what technology it would take to achieve that outcome.

Monitoring a compressor

I decided to see how I might do this for a simple example: a compressor. Compressors are very common in most industrial facilities and buildings, can be instrumented fairly easily, and are great candidates for predictive maintenance—one of the terms you hear a lot in the context of IoT.

So, my desired outcome would be to move from preventive maintenance to predictive, as well as to eliminate the unnecessary costs that often result from scheduled maintenance (yes, there is such thing as too much maintenance).

The compressor I looked at is a 40 hp rotary screw compressor, and a great example of legacy equipment you might want to get data from. It has visual analog gauges for some parameters, but no connection of these values to the outside world.

The facilities manager responsible for this compressor told me they perform preventive maintenance about once a year, and we found a report from the most recent service. I verified with the manufacturer's documentation that these items lined up with yearly service recommendations. There were also other checks and maintenance items recommended at weekly, monthly and three-month intervals.

What values to monitor?

To move from this periodic compressor maintenance to predictive maintenance, the three parameters I decided to monitor were motor temperature, vibration and motor current.

These parameters are good indicators of compressor health, are simple to start with, and can be instrumented without much effort or cost—and without taking the machine apart.

Tracking differential pressure across the compressor’s fluid would also give us great data to trigger filter replacement, but measuring it might require pipework. It was decided that tracking motor current and, perhaps, adding temperature sensors on the fluid pipes would suffice for now.

For temperature, all we're looking for is a trend, so a simple Type J or K thermocouple could be mounted on the motor housing. This would be wired to a SNAP-AITM thermocouple input module.

Next, we would need a vibration sensor with a 4-20 mA output. These sensors are widely available, inexpensive and could be mounted on the motor and other areas. We would need 12-30 VDC to power the loop, and the 4-20 mA signal would be wired to a SNAP-AIMA module.

Next, we could measure the motor’s three-phase current by using three split-core current transformers installed at the compressor disconnect switch. The motor is rated at about 90 A, so we'd select appropriate current transformers (CTs) with an adequate inner diameter to accommodate the feed wire. Many CTs have a standard 5 A secondary, so we would wire these three signals to two SNAP-AIARMS modules.

To complete our monitoring system, we would need:

  • A four-position I/O rack, SNAP-PAC-RCK4, to mount our modules.
  • A rack-mounted controller such as the SNAP-PAC-R2.
  • A power supply to power the monitoring system. Since we will need DC power for the vibration sensor, we could select a SNAP-PS5-24DC.

I noticed an Ethernet switch close by with an available port, so we could put the monitoring system on the plant network. Configuring the I/O modules and points is easy with the free PAC Manager tool.

Getting data where it needs to go

At this point, we would be ready to start getting the data somewhere where we could log it, visualize it and get notifications on anomalies.

This "somewhere" could be on the same network as the monitoring system (that is, at the edge), using something like a groov device. Or it could be in the cloud, using one of the many third-party IoT platforms out there, such as IBM’s Watson. Watson has a basic rules engine for notifications, and the data could also be shared with other Bluemix services for more advanced analytics.

With this simple, inexpensive IoT application of condition-based monitoring built on these three parameters, we should have enough data to move from preventive maintenance to predictive for the compressor.

The project is also scalable. After some experience and insights are gained, additional sensors and instruments could be installed on other pieces of equipment and the monitoring system expanded. It could even include controls to react to actionable intelligence derived from the collected data.

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