A High-Speed, Statistical View for Better Decision Making

You might recall Emerson’s Bill Zhou from a quick, Rosemount transmitter demo video done at the Emerson Exchange a few weeks ago.

I asked Bill if I could get a copy of his and Andrew Klosinski‘s recent National Petrochemical & Refiners Association (NPRA) presentation, Advanced Diagnostics: 4 Steps to Better Decision Making.

The focus is on how advanced statistical process monitoring (SPM) technologies in intelligent field devices can help process manufacturers reduce maintenance costs, improve product quality and increase process uptime. All of that is easy to say, but the good thing is this presentation offers many case studies showing how.

Statistical Process Monitoring at 22 times per second
First, from a technology standpoint, it’s important to understand that a transmitter is much closer to where the action is, than the automation system. It touches the process as it measures temperature, level, flow, pressure, etc. Transmitters like the Rosemount 3051S, measure the process at 22 times per second instead of 1-2 times per second that is typical at the automation system level of the hierarchy. This higher resolution sampling is the basis for the statistical process monitoring to detect abnormal situations.

This statistical trending of process information is step one of the four steps to better decision making. It’s followed by event correlation, then the creation of specific alerts to warn operators and/or maintenance folks, followed by actionable information to correct the situation before the unplanned shutdown, quality excursion, or asset failure occurs.

One example is a plugged impulse line. From a traditional view, an operator might see a quick drop in flow, with the valve position rapidly opening to try to compensate. It might take the operator quite a while to figure out why this occurred. During this troubleshooting period, process oscillations and shutdowns might occur. This same scenario seen from the transmitter’s statistical perspective would show a sharp drop in the standard deviation. This indicates a plugged impulse line condition. In the real case study shared, Bill and Andrew show the dirt that had accumulated inside the pipe wall. Some dirt tore off the wall, which caused the plugging of the impulse line. Since the transmitter shared this insight, the problem was addressed far more quickly than with traditional troubleshooting methods.

Additional SPM-based advanced diagnosis and communication examples included furnace flame instability, DP level agitation loss, pump / valve cavitation, turbine blade wear, pressure transient detection, and distillation column flooding.

The common thread is the high-resolution, statistical monitoring of a process variable (PV) signal to identify and communicate the abnormal situation. In the case of burner flameout, flame instability shows a sharp increase in standard deviation of measured fuel gas pressure.

In the case of distillation column flooding, efficient separation stops, diagnosis is difficult, and repair is time consuming. Looking at differential pressure (DP) measurement across the packing from an SPM perspective shows an increase in standard deviation that correlates as a leading indicator of incipient flooding.

Make sure to view the presentation, if you have any of the other cases not highlighted in this post. Also, I’ll keep working to try to get Bill to share some of these examples in video form, now that he’s a YouTube star!

GreenPodcast.gif MP3 | iTunes

Update: I added a better link to the advanced diagnostics section of the 3051S and fixed the link to the NPRA.

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