Recent advances in sensing technologies and increasing computation power have accelerated the development of condition monitoring systems based on different approaches. There has been intensive research to automate the detection of anomalies in machines and processes by monitoring the changes in collected sensor data. Especially, a disc mower is prone to damage if it is frequently deployed in places where it might hit solid objects such as boulders and old fence posts. These anomalies cannot be easily recognized by the operator and may cause suboptimal results. In this paper, two deep learning models for intelligent condition monitoring in a disc mower are investigated to notify the machine operator when a failure occurs. For this, a basic convolutional neural network (CNN) and a residual neural network (ResNet) were trained, evaluated and the preliminary results are presented.     «

Recent advances in sensing technologies and increasing computation power have accelerated the development of condition monitoring systems based on different approaches. There has been intensive research to automate the detection of anomalies in machines and processes by monitoring the changes in collected sensor data. Especially, a disc mower is prone to damage if it is frequently deployed in places where it might hit solid objects such as boulders and old fence posts. These anomalies cannot be...     »