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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Muniz, P.R. Cani, S.P.N. Magalhaes, R.D.S. |
| Copyright Year | 2001 |
| Abstract | Temperature measurement by infrared thermography is a powerful technique for predictive maintenance. To make accurate diagnoses, errors associated with temperature measurement results should be minimized because the diagnosis is obtained by applying temperature difference criteria in consecutive measurements or in similar equipment. Temperature measurement by infrared thermography is susceptible to errors caused by many things. We study and assess the effectiveness obtained in use of experimental models for error correction in temperature measurement by thermal imagers due to the influence of the field of view of the imager's lens, combined with varying viewing angles between the analyzed object and the imager. The results indicate that these factors can introduce errors that compromise the diagnosis. Experimental models based on artificial neural network and on regression were developed to correct such errors. |
| Sponsorship | IEEE Sensors Council |
| Starting Page | 729 |
| Ending Page | 733 |
| Page Count | 5 |
| File Size | 422260 |
| File Format | |
| ISSN | 1530437X |
| Volume Number | 14 |
| Issue Number | 3 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Temperature measurement Artificial neural networks Measurement uncertainty Uncertainty Thermal sensors Measurement errors Thermal analysis temperature measurement Field of view of lenses infrared imaging error analysis |
| Content Type | Text |
| Resource Type | Article |
| Subject | Instrumentation Electrical and Electronic Engineering |
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