Online validation of the measurement uncertainty of a sensor using wavelet transforms

The measurement uncertainty of a sensor as a measure of its accuracy is normally derived through static analysis. Conventionally, uncertainty analysis is conducted during the design and development stage of a sensor to select appropriate components and measurement techniques, as well as interpreting its experimental data. The demand for quality assurance by measurement is now increasing. It is therefore desirable to develop a means of assessing the measurement uncertainty of a sensor during its operation on a process plant. A new method using wavelet transforms for the validation of the measurement uncertainty is proposed. Analytical results show that the process variable being measured by the sensor can be separated from a noisy raw sensor signal using its wavelet transforms, provided that the process variable is represented in terms of a limited-degree polynomial function. Unlike the conventional approach to uncertainty calculation, which requires 'average' or 'typical' values substituted for parameters which may vary, the proposed method uses only the latest output of the sensor regardless of the variations of the parameters, and thus can be applied on an online continuous basis. Experimental results obtained from a differential-pressure flow sensor on a water flow-test rig confirm the effectiveness of the proposed method.