Determining the convergence of synchronous measurements for embedded industrial applications

Leis, John and Buttsworth, David (2017) Determining the convergence of synchronous measurements for embedded industrial applications. IEEE Transactions on Industrial Electronics, 64 (9). ISSN 0278-0046

Abstract

One technique which may be employed in certain types of sensing application is the phase-sensitive or lock-in method. Since this method essentially trades off longer measurement time for a greater accuracy, it is sometimes necessary to capture samples over a long time period to ensure sufficient stability in the calculated parameter estimate. This may adversely affect safety-critical systems, and some method of determining the relative stability of the measured parameter is needed to permit this useful approach to be more widely employed.
In this paper, we propose and evaluate a novel entropy-based method for ascertaining stability. The contributions of this paper are to firstly highlight the need for a convergence measure which is reliable, automatic, and easily computed; secondly, we propose one such measure, and place it on a theoretical footing; finally, we give results both with simulated Gaussian noise having a typical sensor power spectrum, as well as experimental results using the type of optical sensor which would benefit from the proposed method. It is demonstrated that this approach produces a reliable asymptotic figure for convergence, both under simulated noise conditions as well as with real measurements in the field.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: First place winner for the USQ School-Specific 2016 Publication Excellence Awards for Journal Articles - School of Mechanical and Electrical Engineering. Online version available in IEEE XPlore December 2016. Permanent restricted access to ArticleFirst version in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering
Date Deposited: 12 Jan 2017 01:44
Last Modified: 21 Dec 2017 03:42
Uncontrolled Keywords: signal to noise ratio, entropy, detection algorithms, filtering algorithms, adaptive signal detection
Fields of Research : 09 Engineering > 0906 Electrical and Electronic Engineering > 090603 Industrial Electronics
09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
09 Engineering > 0906 Electrical and Electronic Engineering > 090601 Circuits and Systems
Socio-Economic Objective: B Economic Development > 86 Manufacturing > 8615 Instrumentation > 861501 Industrial Instruments
Identification Number or DOI: 10.1109/TIE.2016.2638798
URI: http://eprints.usq.edu.au/id/eprint/29994

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