Application of Composite Spectrum in Agricultural Machines
Composite spectrum (CS) is a data-fusion technique that reduces the number of spectra to be analyzed, simplifying the analysis process for machine monitoring and fault detection. In this work, vibration signals from five components of a combine harvester (thresher, chopper, straw walkers, sieve box,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/19/5519 |
| _version_ | 1797552483531227136 |
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| author | Fernando Feijoo Francisco Javier Gomez-Gil Jaime Gomez-Gil |
| author_facet | Fernando Feijoo Francisco Javier Gomez-Gil Jaime Gomez-Gil |
| author_sort | Fernando Feijoo |
| collection | DOAJ |
| description | Composite spectrum (CS) is a data-fusion technique that reduces the number of spectra to be analyzed, simplifying the analysis process for machine monitoring and fault detection. In this work, vibration signals from five components of a combine harvester (thresher, chopper, straw walkers, sieve box, and engine) are obtained by placing four accelerometers along the combine-harvester chassis in non-optimal locations. Four individual spectra (one from each accelerometer) and three CS (non-coherent, coherent and poly-coherent spectra) from 18 cases are analyzed. The different cases result from the combination of three working conditions of the components—deactivated (off), balanced (healthy), and unbalanced (faulty)—and two speeds—idle and maximum revolutions per minute (RPM). The results showed that (i) the peaks can be identified in the four individual spectra that correspond to the rotational speeds of the five components in the analysis; (ii) the three formulations of the CS retain the relevant information from the individual spectra, thereby reducing the number of spectra required for monitoring and detecting rotating unbalances within a combine harvester; and, (iii) data noise reduction is observed in coherent and poly-coherent CS with respect to the non-coherent CS and the individual spectra. This study demonstrates that the rotating unbalances of various components within agricultural machines, can be detected with a reduced number of accelerometers located in non-optimal positions, and that it is feasible to simplify the monitoring with CS. Overall, the coherent CS may be the best composite spectra formulation in order to monitor and detect rotating unbalances in agricultural machines. |
| first_indexed | 2024-03-10T16:01:42Z |
| format | Article |
| id | doaj.art-5fd74094f80f465dbd7180cac247266b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:01:42Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5fd74094f80f465dbd7180cac247266b2023-11-20T15:14:12ZengMDPI AGSensors1424-82202020-09-012019551910.3390/s20195519Application of Composite Spectrum in Agricultural MachinesFernando Feijoo0Francisco Javier Gomez-Gil1Jaime Gomez-Gil2Department of Electromechanical Engineering, University of Burgos, 09006 Burgos, SpainDepartment of Electromechanical Engineering, University of Burgos, 09006 Burgos, SpainDepartment of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, SpainComposite spectrum (CS) is a data-fusion technique that reduces the number of spectra to be analyzed, simplifying the analysis process for machine monitoring and fault detection. In this work, vibration signals from five components of a combine harvester (thresher, chopper, straw walkers, sieve box, and engine) are obtained by placing four accelerometers along the combine-harvester chassis in non-optimal locations. Four individual spectra (one from each accelerometer) and three CS (non-coherent, coherent and poly-coherent spectra) from 18 cases are analyzed. The different cases result from the combination of three working conditions of the components—deactivated (off), balanced (healthy), and unbalanced (faulty)—and two speeds—idle and maximum revolutions per minute (RPM). The results showed that (i) the peaks can be identified in the four individual spectra that correspond to the rotational speeds of the five components in the analysis; (ii) the three formulations of the CS retain the relevant information from the individual spectra, thereby reducing the number of spectra required for monitoring and detecting rotating unbalances within a combine harvester; and, (iii) data noise reduction is observed in coherent and poly-coherent CS with respect to the non-coherent CS and the individual spectra. This study demonstrates that the rotating unbalances of various components within agricultural machines, can be detected with a reduced number of accelerometers located in non-optimal positions, and that it is feasible to simplify the monitoring with CS. Overall, the coherent CS may be the best composite spectra formulation in order to monitor and detect rotating unbalances in agricultural machines.https://www.mdpi.com/1424-8220/20/19/5519composite spectrummonitoringvibrationscoherentnon-coherentpoly-coherent |
| spellingShingle | Fernando Feijoo Francisco Javier Gomez-Gil Jaime Gomez-Gil Application of Composite Spectrum in Agricultural Machines Sensors composite spectrum monitoring vibrations coherent non-coherent poly-coherent |
| title | Application of Composite Spectrum in Agricultural Machines |
| title_full | Application of Composite Spectrum in Agricultural Machines |
| title_fullStr | Application of Composite Spectrum in Agricultural Machines |
| title_full_unstemmed | Application of Composite Spectrum in Agricultural Machines |
| title_short | Application of Composite Spectrum in Agricultural Machines |
| title_sort | application of composite spectrum in agricultural machines |
| topic | composite spectrum monitoring vibrations coherent non-coherent poly-coherent |
| url | https://www.mdpi.com/1424-8220/20/19/5519 |
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