Auto-zero/Auto-calibration
From Wikimization
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== Examples == | == Examples == | ||
- | Biochemical temperature control where multiple temperature sensors are multiplexed into a data stream and one or more channels are set aside for Auto-calibration. Expected final systems accuracies of .05 degC are needed because mammalian temperature regulation has resulting in processes and diseases that are "tuned" to particular temperatures. | + | ===Biochemical temperature control=== |
+ | where multiple temperature sensors are multiplexed into a data stream and one or more channels are set aside for Auto-calibration. Expected final systems accuracies of .05 degC are needed because mammalian temperature regulation has resulting in processes and diseases that are "tuned" to particular temperatures. | ||
* A simplified example, evaluating one calibration channel and one reading channel. In order to be more obvious the unknown and calibration readings are designated separately; instead of the convention given above. This is more obvious in a simple case, but in more complicated cases is unsystematic. | * A simplified example, evaluating one calibration channel and one reading channel. In order to be more obvious the unknown and calibration readings are designated separately; instead of the convention given above. This is more obvious in a simple case, but in more complicated cases is unsystematic. | ||
** <math>V_x=V_{off}+\frac{V_{ref} \cdot R_x}{R_x + R_b}</math> | ** <math>V_x=V_{off}+\frac{V_{ref} \cdot R_x}{R_x + R_b}</math> | ||
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- | Infrared Gas analysers with either multiple stationary filters or a rotating filter wheel. In either case the components, sensors, and physical structures are subject to significant variation. | + | ===Infrared Gas analysers=== |
+ | with either multiple stationary filters or a rotating filter wheel. In either case the components, sensors, and physical structures are subject to significant variation. | ||
== Various forms of <math>Q()\,</math> == | == Various forms of <math>Q()\,</math> == |
Revision as of 18:42, 17 August 2010
Contents |
Motivation
In instrumentation, both in a supporting role and as a prime objective, measurements are taken that are subject to systematic errors. Routes to minimizing the effects of these errors are:
- Spend more money on the hardware. This is valid but hits areas of diminishing returns; the price rises disproportionately with respect to increased accuracy.
- Apparently, in the industrial processing industry, various measurement points are implemented and regressed to find "subspaces" that the process has to be operating on. Due to lack of experience I (RR) will not be covering that here; although others are welcome to (and replace this statement). This is apparently called "data reconciliation".
- Calibrations are done and incorporated into the instrument. This can be done by analog adjustments or written into storage mediums for subsequent use by operators or software.
- Runtime Auto-calibrations done at regular intervals. These are done at a variety of time intervals: every .01 seconds to 30 minutes. I can speak to these most directly; but I consider the "Calibrations" to be a special case.
Mathematical Formulation
Let
- a vector of some environmental or control variables that need to be estimated
- a vector of calibration points
- be the estimate of
- a vector of nominal values of uncertain parameters affecting the measurement
- Assumed constant or designed in
- be the errors in
- Assumed to vary but constant in the intervals between calibrations and real measurements
- be the results of a measurement processes attempting to measure
- where might be additive, multiplicative, or some other form.
- the reading values at the calibration points
- Subsequently will be assumed fixed for the problem realm; and dropped from notation
- be estimates of derived from
- be a quality measure of resulting estimation; for example
- Where is allowed to vary over a domain for fixed
- The example is oversimplified as will be demonstrated below.
- is typically decomposed into a chain using
Then the problem can be formulated as:
- Given
- Find a formula or process to select so as to minimize
- The reason for the process term is that many correction schemes are feedback controlled; is never computed, internally, although it might be necessary in design or analysis.
Examples
Biochemical temperature control
where multiple temperature sensors are multiplexed into a data stream and one or more channels are set aside for Auto-calibration. Expected final systems accuracies of .05 degC are needed because mammalian temperature regulation has resulting in processes and diseases that are "tuned" to particular temperatures.
- A simplified example, evaluating one calibration channel and one reading channel. In order to be more obvious the unknown and calibration readings are designated separately; instead of the convention given above. This is more obvious in a simple case, but in more complicated cases is unsystematic.
- can be either the calibration resistor or the unknown resistance of the thermistor
- is the corresponding voltage read:
- is the reading offset value, an error
- the bias voltage
- the bias resistor
- With errors
- Calibration reading
- Thermistor (real) reading.
- The problem is to optimally, an ambiguous term during design, estimate based upon and
- The direct inversion formula illustrates the utility of mathematically using the error space, . during design and analysis. The direct inversion of for naturally invokes the error space as a link to .
- Inversion for
- Inversion in terms of estimates
- Inversion for
- The problem is to minimize some .
- One point, setpoint: when then minimize . This might seem a little strange, but applies when one is trying to set to a setpoint but errors occur during measurement. The division is induced when and is the real variable of interest.
- Mode estimate: Maximize the probability of an estimate treating the calibration measurement as a constraint hypersurface ((n-1)-dimensional foliate in a n-dimensional space) in the error space with a defined PDF function. This can done via KKT; and also extended to more calibration readings. In polynomial cases this can theoretically be solved via Groebner basis; but even given the "exact" solutions, one is forced into sub-optimal/approximate estimates.
- Mean estimate: Using the same model the expected error of a estimate given all possible values on the constraint surface weighted by a PDF distribution on the constraint surface; is minimized. The projection of the original PDF on n-space, to the constraint surface can be done via differential geometry. There are probably statistical methods, but the statistics descriptions seem to take a cavalier attitude towards some transformations involving integrals.
- Worst case: where points considered where the constraint meets some boundary; say +- .01%
- Any of the above extended to cover a range of as well as the range of errors.
- It should be mentioned that, in this case is not a good (or natural) function. A better function for both results and calculations is . I consider the form of errors to be a natural variation from problem to problem and should be accommodated in any organized procedure.
- Sensitivities are needed during design in order to determine which errors are tight and find out how much improvement can be had by spending more money on individual parts; and during analysis to determine the most likely cause of failures.
Infrared Gas analysers
with either multiple stationary filters or a rotating filter wheel. In either case the components, sensors, and physical structures are subject to significant variation.
Various forms of
- Weighted least squares of over the range of
- Minimize mode of with respect to the range of and the measurements
- Minimize the mean of with respect to the range of and the measurements
- Minimize the worst case of over the range of
- Some weighting of the error interval with respect to
Areas of optimization
Design
Runtime
Calibration usage