Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery

Generally, the measured health condition data from mechanical system often exhibits imbalanced distribution in real-world cases. To enhance fault diagnostic accuracy of the imbalanced data set, a novel rotating machinery fault imbalanced diagnostic approach based on Deep Laplacian Auto-encoder (DLapAE) is firstly developed in this paper. First of all, the collected vibration signals are immediately entered into the constructed DLapAE algorithm for layer-by-layer feature extraction, afterwards the extracted deep discriminative sensitive features are flowed into Back Propagation (BP) classifier for health condition diagnosis. More specifically, it is well worth mentioning that Laplacian regularization term can be reasonably added into the original objective function of Deep Auto-encoder (DAE) for smoothing the manifold structure of data in DLapAE. Namely, the proposed DLapAE algorithm with Laplacian regularization can improve the generalization performance of this fault diagnosis framework and make it more suitable for feature learning and classification of imbalanced data. Last but not least, two case of the experimental bearing systems can prove the effectiveness of proposed methodology. Compared with other existing fault diagnosis methods based on deep learning, the proposed fault diagnosis method can effectively implement the accurate fault diagnosis for rotating machinery balanced and imbalanced datasets. (C) 2019 Elsevier Ltd. All rights reserved.