Unsupervised Fault Diagnosis of Machine via Multiple-Order Graphical Deep Extreme Learning Machine

Abstract

The intelligent fault diagnosis powered deep learning (DL) is widely applied in various real industrial fields. However, the classical intelligent fault diagnosis methods cannot fully juggle the manifold structure information with multiple-order similarity from the massive industrial data. At the same time, the scarcity of the labeled information can also result in inferior generalization performance. To this end, a new multiple-order graphical deep extreme learning machine (MGDELM) algorithm for unsupervised fault diagnosis (UFD) of rolling bearing is designed in this study. By jointly optimizing the multiple-order objective function, the proposed MGDELM algorithm can synchronously extract local and global structural information from the raw industrial data. Empirically, rolling bearing failure data validates the effectiveness of the designed algorithm and fault diagnosis method.