Monitoring machine runtime health parameters through nonintrusive means can greatly reduce the up-front time and resource barriers to entry of adding instrumentation to existing plant infrastructure. This work presents the design and evaluation of three transducers as part of a nonintrusive load monitoring system for rotating machinery. Data collected using a custom designed, small-scale induction motor test stand shows the dependence of a large air core RF coil, small RF coil array, and Hall effect sensor outputs on applied motor speeds and mechanical loads (estimated based on generator power). Analysis indicates that the large air core RF coil transducer and the presented method for using nonintrusive collection of induction motor speed and stray flux can statistically measure the difference between any two load points with 95% confidence, if their values differ by 6.6% full scale or greater (±2). Additionally, areas of further research toward generalization of the approach are identified.
How to Cite
nonintrusive load monitoring, stray flux, rotating machinery
Bin Lu, Habetler, T. G., & Harley, R. G. (2006). A survey of efficiency-estimation methods for in-service induction motors. IEEE Transactions on Industry Applications, 42(4), 924–933. doi:10.1109/TIA.2006.876065
Determining Electric Motor Load And Efficiency. (n.d.). U.S. Department of Energy.
Some Notes on Device Calibration. (n.d.). Retrieved January 18, 2012, from http://www.iceweb.com.au/Test&Calibration/NoteDevi ceCalibration.pdf
Tumanski, S. (2007). Induction Coil Sensors - A Review.pdf. Meas. Sci. Technol., (18), R31–R46. doi:10.1088/0957-0233/18/3/R01
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.