Steady-state, Stationarity, Random walk with drift, White-noise, Hypothesis testing, Student's t
Detecting windows or intervals of when a continuous process is operating in a state of steadiness is useful especially when steady-state models are being used to optimize the process or plant on-line or in real-time. The term steady-state implies that the process is operating around some stable point or within some stationary region where it must be assumed that the accumulation or rate-of-change of material, energy and momentum is statistically insignificant or negligible. This new approach is to assume the null-hypothesis that the process is stationary about its mean subject to independent and identically distributed random error or shocks (white-noise) with the alternative-hypothesis that it is non-stationary with a detectable and deterministic slope, trend, bias or drift. The drift profile would be typical of a time-varying inventory or holdup of material with imbalanced flows or even an unexpected leak indicating that the process signal is not steady. A probability of being steady or at least stationary over the window is computed by performing a residual Student t test using the estimated mean of the process signal without any drift and the estimated standard-deviation of the underlying white-noise driving force. There are essentially two settings or options for the method which are the window-length and the Student t critical value and can be easily tuned for each process signal that are included in the multivariate detection strategy.
Original Publication Citation
BYU ScholarsArchive Citation
Kelly, Jeff and Hedengren, John, "A Steady-State Detection (SSD) Algorithm to Detect Non-Stationary Drifts in Processes" (2013). Faculty Publications. 1713.
Journal of Process Control, Elsevier
Ira A. Fulton College of Engineering and Technology
© 2013 Elsevier Ltd. All rights reserved. This is the author's submitted version of this article.
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