A method of measurement selection is introduced that relies on parameter signatures to assess the identifiability of dynamic model parameters by different outputs. A parameter signature is a region in the time-scale plane wherein the sensitivity of the output with respect to one model parameter is much larger than the rest of the output sensitivities. Since a parameter signature can be extracted when the corresponding output sensitivity is independent of the others, the ability to extract parameter signatures is indicative of parameter identifiability by the output and used here for output/measurement selection. The purpose of this paper is to introduce a strategy for measurement selection by parameter signatures and to demonstrate its applicability to the transient decks of turbojet engines. The validity of the selected outputs in providing observability to all the engine model parameters is independently verified by successful estimation of parameters by nonlinear least-squares estimation.
It is often a challenge to gain insight into undergraduate study habits. Students can list the resources at their disposal and can explain the benefits of well-understood techniques (e.g. study groups, individual meetings/tutoring, time management); however, the same students will often ignore the warning signs of academic trouble and resort to poor habits (e.g., web searches for assignment answers). Additionally, students often believe that the knowledge from class doesn't need to be retained beyond one assignment, quiz, or exam, regardless of if they fail the assignment or evaluation.
Model validation is the procedure whereby the fidelity of a model is evaluated. The traditional approaches to dynamic model validation consider model outputs and observations as time series and use their similarity to assess the closeness of the model to the process. A common measure of similarity between the two time series is the cumulative magnitude of their difference, as represented by the sum of squared (or absolute) prediction error. Another important measure is the similarity of shape of the time series, but that is not readily quantifiable and is often assessed by visual inspection. This paper proposes the continuous wavelet transform as the framework for characterizing the shape attributes of time series in the time-scale domain. The feature that enables this characterization is the multiscale differential capacity of continuous wavelet transforms. According to this feature, the surfaces obtained by certain wavelet transforms represent the derivatives of the time series and, hence, can be used to quantify shape attributes, such as the slopes and slope changes of the time series at different times and scales (frequencies). Three different measures are considered in this paper to quantify these shape attributes: (i) the Euclidean distance between the wavelet coefficients of the time series pairs to denote the cumulative difference between the wavelet coefficients, (ii) the weighted Euclidean distance to discount the difference of the wavelet coefficients that do not coincide in the time-scale plane, and (iii) the cumulative difference between the markedly different wavelet coefficients of the two time series to focus the measure on the pronounced shape attributes of the time series pairs. The effectiveness of these measures is evaluated first in a model validation scenario where the true form of the process is known. The proposed measures are then implemented in validation of two models of injection molding to evaluate the conformity of shapes of the models’ pressure estimates with the shapes of pressure measurements from various locations of the mold.
A novel method of controller tuning is introduced to achieve a desired closed-loop response. It uses the same strategy as Iterative Feedback Tuning (IFT), but instead of relying on a scalar cost function of the performance error between the desired response and system response, it utilizes the expanded version of the performance error in the time-scale domain for estimating the suitable controller parameters. The proposed method relies on the enhanced delineation of output sensitivities in the time-scale domain to identify regions in the time-scale domain wherein the performance error can be attributed to individual controller parameters [1]. It then relies on the error association in each region to estimate the corresponding controller parameter. It is shown that given a realistic desired response for the closed-loop system, the proposed method can lead to satisfactory controller parameters. It is also shown that the results from this method can be integrated with those from IFT to represent the best of the two solutions from the time and time-scale domains.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations –citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.