- #1
aydos
- 19
- 2
Problem: I have a sensor monitoring a process which is controlled by a feedback controller. This sensor fails from time-to-time and I need to replace it with a new one. I have always used the same type of sensor, say type A. Some sensor manufacturers are offering me an alternative sensor technology, say sensor type B to measure the same process with the same theoretical signal characteristics. This needs to be tested though. I cannot afford to replace sensor type A with sensor type B and see how the controller performs. What I can do is to install sensor type B to monitor the same process, but remain "offline" (not used by the controller). This allows me to monitor both signals in parallel. By comparing the two signals, how can I determine if sensor type B will not produce negative impacts in my controller?
Current plan:
I am planning to trial sensor type B by installing three sensors monitoring the same process:
Sensor 1) Sensor type A, this is the reference sensor
Sensor 2) Sensor type A, this is candidate #1
Sensor 3) Sensor type B, this is candidate #2
Generate two error time series: one with the error between candidate #1 and reference and the second the error between candidate #2 and reference. The conclusion of a successful trial should be able to state that the differences of both errors are statistically insignificant.
Statistical test:
My first thought was to use a Student's t-test to compare the error signals. But I understand the t-test only tests for differences in mean values. But I suspect, I also need to know if the error variances are the same.
Questions:
- Will the F-test provide a test that is sensitive to both mean and variance differences?
- Would anyone suggest an alternative approach?
- I am collecting data for a 24 hour period. During this time, the process operates under 5 different regimes. Should I break-up the time series into 5 segments and run separate tests?
Will this split approach change the test criteria?
Other info:
- The error signals have a good approximation to a normal distribution
- The measurement noise is not time-correlated
Current plan:
I am planning to trial sensor type B by installing three sensors monitoring the same process:
Sensor 1) Sensor type A, this is the reference sensor
Sensor 2) Sensor type A, this is candidate #1
Sensor 3) Sensor type B, this is candidate #2
Generate two error time series: one with the error between candidate #1 and reference and the second the error between candidate #2 and reference. The conclusion of a successful trial should be able to state that the differences of both errors are statistically insignificant.
Statistical test:
My first thought was to use a Student's t-test to compare the error signals. But I understand the t-test only tests for differences in mean values. But I suspect, I also need to know if the error variances are the same.
Questions:
- Will the F-test provide a test that is sensitive to both mean and variance differences?
- Would anyone suggest an alternative approach?
- I am collecting data for a 24 hour period. During this time, the process operates under 5 different regimes. Should I break-up the time series into 5 segments and run separate tests?
Will this split approach change the test criteria?
Other info:
- The error signals have a good approximation to a normal distribution
- The measurement noise is not time-correlated