Agricultural Vehicle Condition Monitoring System Based On Unsupervised Novelty Detection

A tractor gearbox test rig has been used to collect signals from different types of bearing faults. For vibration monitoring accelerometers have been used to obtain vibration data. For fuel-injectors a bearing checker has been used in order to collect acoustic data. One class Self Organizing Maps (OCSOM) are used for detecting faults when exposed to actual data from the system representing a yet unknown state. Feature extraction was performed using seven features. The feature vectors are then fed to the OCSOM for training. OCSOM classification gave promising results (more than 95% correct classification). The fusion of features from both the vertical and the horizontal accelerometer resulted in accurate fault detection. In the case of the fuel-injectors the feasibility of using one-class SOM has been tested in the detection of signal deviations indicating failure with high detection performance.