Prognosis of bearing and gear wears using convolutional neural network with hybrid loss function

This study aimed to propose a prognostic method based on a one-dimensional convolutional neural network (1-D CNN) with clustering loss by classification training. The 1-D CNN was trained by collecting the vibration signals of normal and malfunction data in hybrid loss function (i.e., classification loss in output and clustering loss in feature space). Subsequently, the obtained feature was adopted to estimate the status for prognosis. The open bearing dataset and established gear platform were utilized to validate the functionality and feasibility of the proposed model. Moreover, the experimental platform was used to simulate the gear mechanism of the semiconductor robot to conduct a practical experiment to verify the accuracy of the model estimation. The experimental results demonstrate the performance and effectiveness of the proposed method.