Leis, John and Buttsworth, David (2017) Determining the Convergence of SynchronousMeasurements for Embedded Industrial Applications. IEEE Transactions on Industrial Electronics, 64 (9). pp. 7392-7398. 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 first highlight the need for a convergence measure which is reliable, automatic, and easily computed; second, 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: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. First place winner for the USQ School-Specific 2016 Publication Excellence Awards for Journal Articles - School of Mechanical and Electrical Engineering. |
| Faculty/School / Institute/Centre: | Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 -) |
| Faculty/School / Institute/Centre: | Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 -) |
| Date Deposited: | 12 Jan 2017 01:44 |
| Last Modified: | 17 Mar 2021 05:01 |
| Uncontrolled Keywords: | signal to noise ratio, entropy, detection algorithms, filtering algorithms, adaptive signal detection |
| Fields of Research (2008): | 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 |
| Fields of Research (2020): | 40 ENGINEERING > 4009 Electronics, sensors and digital hardware > 400907 Industrial electronics 40 ENGINEERING > 4006 Communications engineering > 400607 Signal processing 40 ENGINEERING > 4008 Electrical engineering > 400801 Circuits and systems |
| Socio-Economic Objectives (2008): | B Economic Development > 86 Manufacturing > 8615 Instrumentation > 861501 Industrial Instruments |
| Identification Number or DOI: | https://doi.org/10.1109/TIE.2016.2638798 |
| URI: | http://eprints.usq.edu.au/id/eprint/29994 |
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