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Anomaly detection on the cutter bar of a combine harvester
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: 2022 ASABE Annual International Meeting 2200588.(doi:10.13031/aim.202200588)
Authors: Jorre Goossens, Bart Lenaerts, Steven Devos, Konstantinos Gryllias, Bart De Ketelaere, Wouter Saeys
Keywords: Agriculture, combine harvester, condition monitoring, statistical process control, cyclostationarity
Driven by an increasing food demand and an urgency for intelligent land use, the size and complexity of agricultural machinery have increased significantly. Monitoring machine operation is an important task for the operators. Automatic monitoring systems could lighten their job and pave the way towards fully autonomous machines. Therefore, this paper proposes an automatic condition monitoring system for detecting operation anomalies on agricultural machine components with reciprocal motion, and applies it in a use-case on a combine harvester header cutter bar. Cyclostationary analysis techniques are exploited to develop filtering algorithms to extract informative features, which are monitored in control charts by implementation of statistical process control (SPC). Together with a comparison of several filtering and feature extraction techniques an analysis is provided on the influence of sensor type on anomaly detection performance. Filtering benefit was found to be highly dependent on the considered sensor type, with increases in Matthews correlation coefficient (MCC) ranging from 0 to 44%, resulting in maximal MCC values of 1. Suitable feature calculation resulted in average prediction performance improvements up to 40% in MCC values for both considered sensor types. These results highlight the importance of intelligent sensor selection for condition monitoring purposes on agricultural machinery and the added value of SPC involving cyclostationary analysis techniques for anomaly detection.(Download PDF) (Export to EndNotes)