Abstract
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.
Highlights
Modern agriculture relies on the intensive use of machines [1]
Cases D14 and D18 were choosen as the most representative of the real operation of the harvester, because in both cases all the components under study were activated and working at maximum revolutions per minute (RPM), with all the components balanced in Case 14 and unbalanced in Case 18
The three different formulations of the Composite spectrum (CS) clearly differentiated all the component statuses of the thresher, the chopper, the straw walkers and the sieve box, except for the activation versus the deactivation status of the chopper and the thresher at maximum RPM, which was not so clear in the non-coherent CS for the chopper, and poly-coherent CS for the chopper and the thresher. These results suggest that the coherent CS
Summary
Reliance on human operators for the detection of malfunctions is more difficult [2]. Agricultural machines, such as combine harvesters, are complex machines with multiple shafts rotating at different speeds, connected to the engine through pulleys and belts, and supported by bearings [3]. Predictive maintenance is based on the surveillance of the machines during their operation, in order to detect the faults prior to breakage [5] In this way, interventions can be scheduled to prevent failure during the operation of the machine, reducing the maintenance costs [6]
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