Calibration Interval

Algorithm for determining or confirming a calibration interval.

With this proposal, you will be able to determine a new calibration interval (NIC) for a scientific equipment based on information from previous calibrations and taking into account several weighted factors. The goal is to dynamically adapt the calibration interval based on the past performance of the equipment, rather than limiting itself to a fixed interval. This allows to optimize the frequency of calibrations by adjusting the interval based on the stability of the equipment and its reliability over time.

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Explanation of the formula

The algorithm is based on the following formula:

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NIC = AIE x ((P1 x A) + (P2 x B) + (P3 x C))

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• NIC: The New Calculated Interval. This is the optimal time interval proposed for the next calibration.

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• AIE: The Old Calibration Interval. This is the interval used so far for calibrating the equipment. This is the time currently set to recalibrate the equipment.

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• P1, P2, P3: The weights applied to the last three calibrations. These weights are factors adjusted according to the importance given to recent performance.

For example, P1 could be higher than P2 and P3, because the most recent calibration is often considered more representative of the current state of the equipment.

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• A, B, C: The results of the last three calibrations (for example, compliance with tolerances, deviation from standard, or other performance indicator).

These values ​​reflect the quality and accuracy of each calibration and are used to adjust the interval based on past performance.

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Algorithm Terms Details

• AIE (Old Calibration Interval): This is the current calibration interval, determined based on previous practices or manufacturer recommendations. This term represents the basis on which the adjustment is made.

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• P1, P2, P3 (Weightings): These weightings allow to give more or less importance to the results of the last three calibrations. For example, if P1 = 0.5, P2 = 0.3, and P3 = 0.2, this means that the last calibration (P1) has a dominant influence on the calculation of the new interval, while the previous calibrations have a decreasing importance.

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• A, B, C: These values ​​represent the state of the equipment during past calibrations.

They can reflect criteria such as:

o The compliance of the equipment with the calibration standards (for example, if the equipment remained stable within its tolerances).

o The drift observed between calibrations.

o The maintenance history or repairs carried out between two calibrations.

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Algorithm Interpretation

The idea behind this algorithm is that if the equipment shows high stability over several consecutive calibrations (i.e., A, B, and C show good results with little or no drift), it is possible to increase the calibration interval to optimize costs and minimize downtime. In this case, the NIC would be higher than the AIE.

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Conversely, if the results of recent calibrations show increased deviation or instability (e.g., A, B, or C show drift out of tolerance), the NIC would be lower than the AIE, suggesting that it is better to reduce the calibration interval to ensure optimal performance of the equipment.

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Application Example

Let's say the AIE is currently 12 months old, and the results of the last three calibrations show acceptable performance, but with a slight drift over the previous two calibrations.

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The weights are assigned as follows:

• P1 = 0.5 (most recent calibration),

• P2 = 0.3 (previous calibration),

• P3 = 0.2 (even older calibration).

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The results of the calibrations are as follows:

• A = 1 (the most recent calibration was perfectly compliant),

• B = 0.9 (slight drift on the previous calibration),

• C = 0.85 (slightly greater drift on the even older calibration).

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The algorithm would give the following calculation:

NIC = 12 months x ((0.5 x 1) + (0.3 x 0.9) + (0.2 x 0.85))

NIC = 12 months x (0.5 + 0.27 + 0.17)

NIC = 12 months x 0.94

NIC = 11.28 months

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In this example, the NIC is slightly lower than the AIE (11.28 months instead of 12 months), suggesting that the equipment has shown a slight drift and that the calibration interval should be slightly reduced to ensure continued accuracy.

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Conclusion

This algorithm allows a dynamic and adaptive approach to calibration intervals. By weighting the results of the last calibrations, it takes into account the recent performance of the equipment to adjust the calibration interval in an optimized way. If the performance is stable, the interval can be extended. On the other hand, if the equipment shows signs of drift, the interval is reduced to maintain accuracy and reliability.