Enhancing SDP-CNN for Gear Fault Detection Under Variable Working Conditions via Multi-Order Tracking Filtering

Abstract

In the field of gear Fault Detection, the Symmetrized Dot Pattern (SDP) technique, combined with a Convolutional Neural Network (CNN), is widely used to classify various types of defects. The SDP-CNN combination is used to transform vibration signals and simplify the defect classification process under stationary operating conditions. This work aims to enhance the SDP-CNN combination for detecting incipient defects in gear under variable working conditions. The vibration signals are filtered by Vold-Kalman Filter Multi-Order Tracking to highlight fault characteristics under variable working conditions. Subsequently, the signals are SDP-transformed and following classified by optimized CNN. The new pipeline has been validated on an experimental dataset and compared with the classical one by developing both two and multi-class CNNs. The results showed the applicability of the new pipeline in terms of percentage accuracy and ROC curve compared to the classical approach. Finally, the proposed pipeline was compared with other ML literature techniques using the same dataset.