Multiscale Representations Fusion With Joint Multiple Reconstructions Autoencoder for Intelligent Fault Diagnosis

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Abstract
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.
Funding Information
  • National Natural Science Foundation of China (51435011, 51505309)
  • Sichuan Province Science and Technology Support Program Project (2018JY0588)

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