How do I state uncertainty when calibrating a flowmeter?

In summary, the conversation is about setting up a process for calibrating turbine flowmeters for use in fuel rigs. The current process involves taking 10 points up and down the range and using the least squares method to find the line of best fit for calibration. Any points more than 0.5% from the line are considered outliers, with a limit of 2 points between 0.5% and 1%. The speaker suggests using the grubb outlier method and working with multiples of the standard deviation or mean squared error to improve the process. They also mention using Labview to capture data and determine if the calibration has passed or failed.
  • #1
robsmith82
17
0
Hi,

I'm having a lot of trouble trying to set up a process for calibrating turbine flowmeters for use in fuel rigs.

Basically, I have a calibrated "master" flowmeter in series with the uncalibrated turbine flowmeter. I need a process that will give me the line of best fit across the flowmeters range, and also state its uncertainty to a given confidence level (probably 95%)

At the minute, our process isn't very good. We take 10 points going up the range and 10 points down, then take the line of best fit using the least squares method, which gives you your gain and offset for calibration. Then we say, if any point is more than 0.5% of full scale from the line, the calibration has failed. We take outliers into account by saying you are allowed 2 points between 0.5% and 1%, but not at the top and bottom limits and not consecutive. I think we should be using the grubb outlier method.

What I think I have to do is find the mean and SD for each value up the range, then use the worst case SD to calculate the uncertainty. This needs to be carried out by shop floor personnel, so can't be too complicated. We are using labview to capture the data, plot the line and show "pass" or "fail"

Help me out guys!
 
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  • #2
Instead of working with absolute magnitudes (0.5% of the true line) you can find the SD of the sample then work with multiples of the SD; e.g. define an outlier as > 2*SD. Or you can work with mean squared error, which takes into account the bias and the SD.
 

1. How do I determine the uncertainty in my flowmeter calibration?

The uncertainty in flowmeter calibration can be determined using a variety of statistical methods, such as the propagation of uncertainty or Monte Carlo simulation. It is important to consider all sources of uncertainty, such as equipment limitations, environmental conditions, and human error, in order to accurately determine the overall uncertainty in the calibration process.

2. What are the units of uncertainty in flowmeter calibration?

The units of uncertainty in flowmeter calibration are typically the same as the units of the measured quantity, such as liters per minute or gallons per hour. This allows for a direct comparison between the uncertainty and the measured value.

3. How does the type of flowmeter affect the uncertainty in calibration?

The type of flowmeter can significantly affect the uncertainty in calibration. Different types of flowmeters have different sources of error and may require different calibration methods. It is important to understand the specific characteristics of your flowmeter in order to properly account for uncertainty in the calibration process.

4. Can I reduce the uncertainty in flowmeter calibration?

Yes, the uncertainty in flowmeter calibration can be reduced by using more precise equipment, controlling environmental factors, and repeating the calibration multiple times. It is important to carefully document all steps and sources of uncertainty in order to accurately assess the overall uncertainty in the calibration process.

5. What is the acceptable level of uncertainty in flowmeter calibration?

The acceptable level of uncertainty in flowmeter calibration may vary depending on the specific industry or application. It is important to consult relevant standards and guidelines to determine the acceptable level of uncertainty for your specific situation. In general, a lower uncertainty is desirable as it indicates a more accurate measurement.

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