Abstract:
With the advent of Computer based Control Systems, e.g., Distributed Control System
(DCS), process industries are storing huge amount of data everyday. For example, if
data is collected at a sampling rate of 4s, there will be 21,600 samples for each variable
every day. These data are a rich source of information. Often these routine operating
data are used for calculating or estimating various performance metrics of the control
loops or the process. Sometimes, conventional approach of computing a performance
metric using all available data points may lead to erroneous conclusion due to the presence
of various unexpected events, such as abnormal pulses, temporary sensor failures,
transmitter failures, non-stationary trends or process disturbances.
In this study, novel data segmentation methods have been developed to increase
the reliability of data driven process analyses. Calculation of a performance measure
or metric requires only a few hundred or a couple of thousand data points. Therefore,
available data can be divided into several windows for metric estimation.
A three steps novel data segmentation method has been developed. In the first step, a
required data window length is determined for estimating a performance metric. In the
second step, the time series is divided into several data windows for metric estimation.
Finally, an index named reliability index (RI) is calculated from the results obtained in
different data windows. Reliability index (RI), bounded between 0 and 1, calculates
the probability of getting similar results in different data windows. A higher value of
reliability index (RI) indicates more reliable estimate of the metric. The proposed novel data segmentation method has been used successfully for oscillation detection, nonlinearity
analysis and performance index calculation from routine operating data.
