A Novel Sparse Echo Autoencoder Network for Data-Driven Fault Diagnosis of Delta 3-D Printers

Abstract

Fault diagnosis is of importance to guarantee the printing quality and to avoid unexpected downtime for 3-D printers. In this paper, a sparse echo autoencoder network (SEAEN) is proposed for the fault diagnosis of delta 3-D printers using attitude data. Considering the practicality and economy of the fault diagnosis, the attitude data, including three-axial angular velocity signals, three-axial vibratory acceleration ones, and three-axial magnetic field intensity ones, are collected by installing a low-cost attitude sensor on the moving platform of the delta 3-D printer. However, the low-cost sensor will increase the chaos of the attitude data. To make up this deficiency, the SEAEN approach featuring a sparse autoencoder (SAE) combined with an echo state network (ESN) is designed for the fault diagnosis. The SAE is employed to automatically learn features from the high-dimensional attitude data of the delta 3-D printer, while the ESN is used for fault recognition based on the extracted features. The diagnosis performance of the address approach was evaluated in the experiments and its superiority was demonstrated through comparing with other intelligent fault diagnosis techniques.