How to detect unwanted excesive ripple or overshoots on a process variable?
How to detect unwanted excesive ripple or overshoots on a process variable?
(OP)
I'm trying to implement and algorithm which should have the capability to detect some types of anomalies or failures on a process variable like ripples or overshoots. First of all, I want to adjust my process signal with some type of mathematical function, I was thinking on a low pass filter or another type of filter or a moving average, you may notice that my data comes from field sensors such as flowmeter or thermocouples. Once I have my signal adjusted I would like to calculate the deviation between the real process variable and the adjusted smooth curve I have calculated. Then I would create some type of indicator with which I can monitor the deviation between those signals and set up alarms of wrong behavior, when an excessive ripple or an excessive overshoot occur. Could anyone help me with the mathematical function to adjust the data, like low pass filters or whatever?
Cheers.
Cheers.





RE: How to detect unwanted excesive ripple or overshoots on a process variable?
TTFN (ta ta for now)
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RE: How to detect unwanted excesive ripple or overshoots on a process variable?
Cheers
RE: How to detect unwanted excesive ripple or overshoots on a process variable?
Keith Cress
kcress - http://www.flaminsystems.com
RE: How to detect unwanted excesive ripple or overshoots on a process variable?
I would look at Luenberger Observers to start or maybe Kalman filters but Kalman filters required a lot more processing power.
We do what you are trying to do in our motion controllers. We can predict the next position and if it falls outside a range we call that a feedback error.
Peter Nachtwey
Delta Computer Systems
http://www.deltamotion.com
http://forum.deltamotion.com/
RE: How to detect unwanted excesive ripple or overshoots on a process variable?
The low pass "filter" or running average (discrete sampling) is commonly used as you have suggested.
If you want to monitor the deviation of his "average over several time samples", you have to set up a sample matrix (time vs average process data, and the individual time sample), then calculate the running deviation.
Ironically, it is the first step to a Hilbert-Huang transform commonly used to investigate the process characteristics. Hilbert-Huang transforms are more effective in identifing the characteristics of system dynamics as it tolerates the random changes so common in process plants.
d
RE: How to detect unwanted excesive ripple or overshoots on a process variable?
The Levenberg-Marquardt system identification technique returns a norm that can be converted into a standard deviation as well as generate a model.
Peter Nachtwey
Delta Computer Systems
http://www.deltamotion.com
http://forum.deltamotion.com/
RE: How to detect unwanted excesive ripple or overshoots on a process variable?
RE: How to detect unwanted excesive ripple or overshoots on a process variable?
Sounds like the original post was trying to refine his estimate of deviation by filtering the data stream, but basically accomplishing what you've stated
RE: How to detect unwanted excesive ripple or overshoots on a process variable?
RE: How to detect unwanted excesive ripple or overshoots on a process variable?
RE: How to detect unwanted excesive ripple or overshoots on a process variable?
Lag is a direct consequence of the low pass cutoff frequency. The inverse of the cutoff frequency is inversely proportional to the time constant.
TTFN (ta ta for now)
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RE: How to detect unwanted excesive ripple or overshoots on a process variable?
RE: How to detect unwanted excesive ripple or overshoots on a process variable?
"Ok I think I know what you are trying to do. You have just the process variable, and wish to detect deviations from what is should be. So assuming that the system is controller by a half way decent controller, I suggest that you use the output from a low pass filter as your reference. Compare that to the instantous process variable value to get you deviation values. Yes the output of the low pass filter will lag, but that will only be an issue when the set point of the process is changed. As long as the set point remains fixed, the average or low pass filter output should be very close to the setpoint."][/quote]
Yes all what you said is perfect in the case that my system had a constant set point, but this is not the case, the set point is continuously changing, so the low pass filter approach should be ruled out.
RE: How to detect unwanted excesive ripple or overshoots on a process variable?
TTFN (ta ta for now)
I can do absolutely anything. I'm an expert! https://www.youtube.com/watch?v=BKorP55Aqvg
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