期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 67, 期 3, 页码 2360-2370出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2019.2905830
关键词
Geometry; Cost function; Feature extraction; Fault diagnosis; Training; Matrix decomposition; Learning systems; Autoencoder (AE); machine fault diagnosis; neural networks; representation learning
资金
- EEE-Delta Joint Laboratory on Internet of Things [M4061567]
Recently, deep learning-based representation learning methods have attracted increasing attention in machine fault diagnosis. However, few existing methods consider the geometry of data samples. In this paper, we propose a novel method to obtain representations that preserve the geometry of input data. More specifically, we formulate two cost functions to preserve the local and global geometries of input data, respectively and another cost function to reconstruct the input data. Furthermore, to simplify the training process, we formulate a discrimination cost function based on the label information. By jointly optimizing all cost functions, the method can efficiently learn discriminative representations with the local and global geometry of input data preserved. Furthermore, the proposed method can obtain hierarchical representations without any additional tuning step. On two benchmark datasets, the proposed method demonstrates better fault classification performance and shorter training and test time. Therefore, it is an efficient tool to provide accurate information about machine conditions for making maintenance decision and saving costs.
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