Deep Coupling Autoencoder for Fault Diagnosis With Multimodal Sensory Data

Effective fault diagnosis of rotating machinery has multifarious benefits, such as improved safety, enhanced reliability, and reduced maintenance cost, for complex engineered systems. With many kinds of installed sensors for conducting fault diagnosis, one of the key tasks is to develop data fusion...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2018-03, Vol.14 (3), p.1137-1145
Main Authors: Ma, Meng, Sun, Chuang, Chen, Xuefeng
Format: Article
Language:English
Subjects:
Coupling
Coupling autoencoder (CAE)
Couplings
Data integration
Data models
deep learning
Fault diagnosis
Feature extraction
Handles
Machinery
Maintenance costs
multimodal information fusion
Multisensor fusion
Reliability engineering
Rotating machinery
Sensors
Statistical methods
Vibrations
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Summary:Effective fault diagnosis of rotating machinery has multifarious benefits, such as improved safety, enhanced reliability, and reduced maintenance cost, for complex engineered systems. With many kinds of installed sensors for conducting fault diagnosis, one of the key tasks is to develop data fusion strategies that can effectively handle multimodal sensory signals. Most traditional methods use hand-crafted statistical features and then combine these multimodal features simply by concatenating them into a long vector to achieve data fusion. The present study proposes a deep coupling autoencoder (DCAE) model that handles the multimodal sensory signals not residing in a commensurate space, such as vibration and acoustic data, and integrates feature extraction of multimodal data seamlessly into data fusion for fault diagnosis. Specifically, a coupling autoencoder (CAE) is constructed to capture the joint information between different multimodal sensory data, and then a DCAE model is devised for learning the joint feature at a higher level. The CAE is developed by coupling hidden representations of two single-modal autoencoders, which can capture the joint information from multimodal data. The performance of the proposed method is evaluated by two experiments, which shows that the DCAE model succeeds in efficiently utilizing multisource sensory data to perform accurate fault diagnosis. Compared with other methods, the proposed method exhibits better performance.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2018.2793246