The topic is – as easily seen from length and number of answers – not easy. This also means it does highly depend on every specific case to answer this topic.
If we define that the process-parameter ( $p$ ) hast to be monitored. The measurement ( $m$ ) of the process parameter always contains an uncertainty ( $u$ ). So lets state the following model:
$m = p + u$
The purpose of every calibration is to specify the uncertainty. Strictly spoken the uncertainty can only be specified for the calibration which was already done. However, a very reasonable assumption is, that the uncertainty will not change dramatically. But, it will change over time. So the model will look like this:
$m = p + u(t)$
Your proposed redundancy has different effects on the uncertainty, depending on the way redundancy is achieved. One way could be to measure the same process parameter with one additional sensor of the same type (1). Another way would be to either use a different sensor (2) or even observe a different process parameter with a different sensor (3).
Just using the same sensor to observe the same parameter will most likely have a similar degradation of the calibration quality. Because the cause of the degradation is not changed, so both sensor signals have possibly a higher uncertainty over time.
The two other options have a potential to come up with an uncertainty-model which would not make the calibration un-necessary but might increase re-calibration periods.
In any case there is a significant difference between error and uncertainty. If it would be possible to know the error of every measurement one could easily compensate for that.
But the only thing a calibration can provide is the uncertainty of a measurement. Or put it the other way around a calibration will provide you with an estimate how certain one can be that in (usually) 95% of all measurements the returned value will be in the specified interval around the true value.
The following figure shows the upper and lower bounds of the uncertainty assuming a very simple degradation model.
Assuming you want to keep your process parameter at a level of "1" and your uncertainty has an initial value of +/-10% (which is huge but nicer to illustrate). The reason you have to control parameter in the first place is because you need to prove that your process (or product) has a specific quality. As it can be easily be seen without re-calibration (bounds m+ and m-, dotted) after some imaginary time the uncertainty has steadily increased. By re-calibrating in the middle of the interval you reduce the uncertainty (m+r and m-r, solid).
From your description I assume that you need to report or save the calibration before and after every period together with the measurement-logs of the process to prove your quality of the whole production cycle.
It is absolutely understandable to reduce the effort and associated costs of recalibration. The only way to do it is do gather more knowledge of this specific process.
Right now the brute-force approach is to calibrate two times and assume the uncertainty in between those calibrations was within in the two calibrations.
If it would be possible to obtain a better model for the uncertainty degradation the number of calibrations could be reduced. However, in order to have a better model it is necessary to combine a larger number of calibrations. One possible way might be to use all the calibrations and recalibration available. It might be possible to develop a better understanding of the uncertainty and thereby develop a better model which would extend the calibration intervals.
For example given the sensors would degrade in the manner depicted in the figure above, a solution could be to change the control parameters of the production system control over time. But the way to do this or if this is even possible highly depends on your process which we don't know yet.
Finally, even though it is counterintuitive having redundant sensors does not necessarily decrease uncertainty.
By adding an additional sensor of the same type to the system you basically double the number of measurements keeping the same uncertainty. The only way a redundant sensor reduces uncertainty is when the first sensor measurement was not representative of the system and did not only have uncertainty from the sensor but additional fluctuations from the system itself. Think of a measurement like shooting at a target. Depending on your skills you will have a certain probability of hitting the bulls eye. That means by shooting more often you only increase of hitting the bulls eye but you never decrease the spread of your shots. Every shot has the same probability of hitting the bulls eye. Likewise measuring more often increases the chance of measuring the "right" value, but this is not of interest here. It is of interest how certain you can be that every measured value is within a certain "interval around" the right value.
Summing it up:
Adding sensors to a measurement does not necessarily decrease the uncertainty of the combined measurement.
Having two sensors of the same type will not change the degradation rate of the combined measurement value compared to a single sensor.
If the possibility is big that one sensor would return erroneous values during the production run, than having redundant sensors makes a lot of sense. But, this has nothing to do with the uncertainty of the values. In this case the post-calibration would reveal that the process was not properly monitored due to sensor failure so there is not way to assure the quality of the production run at all. In case of sensor failure the other sensor would have kicked in and provided (at best) the same quality as if one sensor would have survived the whole production run.
The only way to reduce the number of calibrations is to collect all calibrations from the past and try to build a sensor-degradation-model from that which might show that the current number of calibrations is unnecessarily high.
By utilising different sensor types or measurement of process parameters it might be possible to come up with a uncertainty model which degrades slower and is more robust with respect to sensor failure.