Deep Learning-Based Approaches for Fault Detection in Disc Mower

With the availability of sensor data and increased processing power, data-driven approaches have been widely investigated to improve various production processes. It is easier to keep track of faults that are hard to perceive during an operation through data-driven conditionmonitoring systems. In addition, the operator or the supervisor of the processes can recognize the need for service in time and provide maintenance. A defect in agricultural machinery can reduce the quality of fieldwork noticeably and cause damages to the crops as well as to the machine itself. Moreover, such damages and low-quality work in agriculture are expensive to fix and sometimes even irreversible. Thus, it is crucial to develop intelligent condition monitoring systems for agricultural machinery. Specifically, machines with rotating components, such as disc mowers, are prone to damage if they are frequently deployed in places where they might hit solid objects. These anomalies cannot always be easily recognized by the operator and may cause suboptimal results. This paper proposes concepts of condition monitoring for a disc mower to optimize the mowing operation. In this study, the first concepts of deep learning-based systems were developed to provide notifications to the operator when a failure occurs     «

With the availability of sensor data and increased processing power, data-driven approaches have been widely investigated to improve various production processes. It is easier to keep track of faults that are hard to perceive during an operation through data-driven conditionmonitoring systems. In addition, the operator or the supervisor of the processes can recognize the need for service in time and provide maintenance. A defect in agricultural machinery can reduce the quality of fieldwork notic...     »