Reduce Calibration Costs and Improve Sensor Integrity through Redundancy and Statistical Analysis

By Dan Collins, Manager of DCS Solution Partner Program, Siemens Energy & Automation It charts the absolute value of the differences between Sensor A and failing Sensor B. Again, sensor B’s oldest 500 values are identical to the first test. Sensor B’s most recent 500 values have been altered by subtracting 20% of the random number to the sensor's value. I.E. Sensor B values for 1 to 500 are 99.5 + random B. Sensor B values from 501 to 1000 are 99.5 + random B - .2 * random B. Notice that a drop in the responsiveness (randomness) of the signals by a mere 20% can be detected. You will notice that the there is at least one alarm (as expected) because the simulated signal noise has diminished to simulate that Sensor B is losing sensitivity—becoming becoming flatlined. Again, notice the sensitivity. The Xbar/R Chart detected drift when the mean difference between the Sensor A values and the Sensor B values closed by a mere 0.012%. In other words, the control chart detected a change in the comparable performance of the two sensors when the noise of one instrument decreased by only 20%. Both instruments are still within calibration, but one is beginning to lose its integrity. If we eliminate 90% of the noise from sensor B, simulating a non-responsive, flatline, comatose instrument, we get the following. See Figure 8. Again, the mean difference has not drifted too far apart but the statistical analysis has recognized the lack of responsiveness. It is now time to calibrate. Sensor reliability and data integrity is assured as long as the pairs of sensors continue to pass the statistical analysis review. When they fail, an alarm generated by the real-time control chart can be triggered through the process control alarm management system. Thus, there is