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.

Existing intelligent fault diagnosis methods depend mostly on single-scale vibration signals, which not only ignores the latent useful information of other different scales, but also underestimates the complementary benefits across scales. In this work, we show the advantage of learning combination of multi-scale information by aiming to automatically capture complementary and discriminative feature representations from different scales of vibration signals. Specifically, we combine the merit of different activation functions in a joint learning fashion and propose a novel joint multiple reconstructions autoencoder (JMRAE), whose training objective is to jointly optimise multiple reconstruction losses. JMRAE aims to jointly learn discriminative and robust scale-specific feature representations. In addition, we design a new multi-scale representations fusion network (MRFN) model to effectively fuse multi-scale feature representations learned concurrently by per-scale JMRAE model for maximising the discriminative capability of the scale-fused features. The objective of MRFN is to benefit all different scales from each other for improving the classification performance. Extensive experimental results demonstrate the effectiveness of the proposed method on rolling bearing dataset.