2 - Measurement Errors and Error Reduction Techniques

Abstract

This chapter introduces the statistical characterization of measurement errors and various univariate error-reduction techniques. The chapter describes data filtering, which is widely used for data conditioning. Various filtering techniques are presented and compared. Individual filtering techniques can be used for error reduction in process measurements, but they are not easy to tune. Some reduce the errors significantly, but with large delay. Others have less delay, but overshoot/undershoot after a true step process change. Measurement errors are random and small (random errors), while others are systematic and large (gross errors). It is generally observed that if the measurement of a process variable is repeated under identical conditions, the same value is not obtained. This is due to the presence of random errors in measurements. Random errors can be neither predicted nor accurately explained. Analog and digital filters have been widely used to reduce random errors (high-frequency noise) in process values. Gross errors are associated with sensor faults; they significantly affect the accuracy of any industrial application using process data. They have to be detected and removed by using statistical quality control (also known as statistical process control).