Representation Learning With Class Level Autoencoder for Intelligent Fault Diagnosis

Although representation learning has been proved to be a promising and effective solution for intelligent fault diagnosis, existing methods still encounter classification performance degradation due to large intra-class variations of real-world applications. In this work, we present a novel representation learning method, namely class level autoencoder (CLAE) for fault diagnosis which aims to learn representative and discriminative features from vibration signals. Specifically, we formulate a novel loss function for representation learning by jointly minimizing the basic and class level reconstruction errors to restrain intra-class variations in the feature space. In addition, we propose a new and simple feature pooling strategy to effectively fuse various meaningful local features to capture efficient and inherent fault pattern information about the input. Extensive experimental results on rolling bearing dataset demonstrate that our proposed method achieves very competitive results compared to state-of-the-art methods. The code of the proposed method is available at https://github.com/KWflyer/CLAE.