The fault diagnosis of rolling bearing has attracted increasing attention in recent years on account of the significant impact on the functionality and efficiency of complex primary system. In consideration of the bearing samples with incomplete labels, this paper investigates the possibilities of a novel fault diagnosis method using the experience of image cognition theory in dealing with the fault state classification of rolling bearings, aiming to realize fault classification that only utilizes a small amount of labeled bearing data. In this paper empirical mode decomposition (EMD) is firstly applied to the original signal, where the basic time domain features are extracted from the first three intrinsic mode functions (IMFs), and are set as the inputs of the following classifier for final training and testing. Weakly labeled support vector machine (WELLSVM), which seems more efficient than inductive support vector machines especially in the case of very small training sets and large test sets, is then established via a novel label generation strategy in the method of semi-supervised learning. Validation data are collected to facilitate the comparison and evaluation of the fault diagnosis results, of which the labeled data proportion is diverse from each other. The results indicates the effectiveness of the proposed method for bearing fault diagnosis with weakly labeled data
