Quantisation and data quality: Implications for system identification. (April 2016) - Shardt, Yuri A.W.; Yang, Xu; Ding, Steven X.
Highlights: Automated extraction of data from a data historian can speed up the creation of models. An issue in this process is inappropriate data quantisation. The impact of data quantisation on the identifiability of the process is provided. An appropriate index for determining the degree of data quantisation is proposed. This index is validated using simulations and actual process data from a CSTH. Abstract: In the pursuit of online, data-driven process control, there is a need to determine the quality of the data being processed before actually using it. One area that needs to be considered is data quantisation. Although in many applications it has been assumed that the impact of quantisation is to solely increase the variance of the signal, in certain cases this may not hold. This is especially the case when dealing with signals from poorly quantised sources, such as temperature sensors. In this case, the effect of quantisation cannot be solely considered by the impact of the increase in the variance. Therefore, this paper will examine the effects of small scale quantisation with the view of determining an appropriate metric for measuring the effect on data quality of quantisation. It will be shown that if the ratio of the unquantised signal variance and the distance between quantisation step sizes are below a given threshold, then the identification of the process parameters will be problematic. Detailed numerical simulations as well as an example drawn from a real system are presented to validate the proposed metrics and approach.