Steady state identification for on-line data reconciliation based on wavelet transform and filtering

In order to derive higher value operational knowledge from raw process measurements, advanced techniques and methodologies need to be exploited. In this paper a methodology for online steady-state detection in continuous processes is presented. It is based on a wavelet multiscale decomposition of the temporal signal of a measured process variable, which simultaneously allows for two important pre-processing tasks: filtering-out the high frequency noise via soft-thresholding and correcting abnormalities by analyzing the maximums of wavelet transform modulus. Wavelet features involved in the pre-processing task are simultaneously exploited in analyzing a process trend of measured variable. The near steady state starting and ending points are identified by using the first and the second order of wavelet transform. Simultaneously a low filter with a probability density function is employed to approximate the duration of a near stationary condition. The method provides an improvement in the quality of steady-state data sets, which will directly improve the outcomes of data reconciliation and manufacturing costs. A comparison with other steady-state detection methods on an example of case study indicates that the proposed methodology is efficient in detecting steady-state and suitable for online implementation.