Attitude data-based deep transfer capsule network for intelligent fault diagnosis of delta 3D printers

Abstract

Abstract In order to improve the quality of printed products and promote the application of 3D printing, it is necessary to carry out health monitoring and fault diagnosis for 3D printers. In this paper, an attitude data-based deep transfer capsule network is proposed for intelligent fault diagnosis of delta 3D printers. Based on the forward kinematic analysis, the attitude data change of the moving platform can reflect the fault information of the printers. To extract fault features from the attitude data with rich directional pose information and complete the cross-domain diagnosis task effectively, the proposed approach consists of a feature encoder with capsule layer, a fault pattern classifier, and a domain discriminator. Through the domain adversarial training, the model can minimize the difference between the source domain and the target domain data distribution, and the trained classifier can obtain better diagnosis performance in the target domain. The experiment result demonstrates the superiority and effectiveness of the proposed method for fault diagnosis problems of delta 3D printers.