Deep subclass reconstruction network for fault diagnosis of rotating machinery under various operating conditions

Abstract In the real-world industrial application, all measured vibration signals are usually suffered from substantial spurious factors and characterized as large intra-class and intra-subclass variations due to changes in the operating conditions (i.e., working loads, shaft speeds), which seriously deteriorates most of the machine learning methods’ ability to learn the discriminative feature representations. In this work, we propose a novel subclass reconstruction network (SCRN) to learn discriminative feature representations from raw vibration signals under different working conditions by suppressing the intra-class and intra-subclass variations in the feature space. Specifically, a novel and simple average strategy is developed to represent the cluster centroid of each subclass information as an effective supervised representation which is effectively embedded into the SCRN model. Furthermore, a new cost function of SCRN is formulated by jointly minimizing the basic and subclass-level reconstruction errors. To better exploit the discriminative information and improve classification performance, we further develop a deep subclass reconstruction network (DSCRN) model by stacking multiple SCRN models together through non-linear transformations to learn better deep feature representations. Extensive comparative evaluations on three benchmarks CWRU, 2009 PHM, and MFPT demonstrate that the proposed methods achieve consistently better diagnosis performance than existing state-of-the-art methods. Especially, when only 1% training samples from CWRU are used, the proposed DSCRN has significant improvement with classification performance of 94.55% over compared method with a gain of at least 9%.