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An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

Sensors (Basel, Switzerland), 2017-02, Vol.17 (2), p.414-414 [Peer Reviewed Journal]

2017. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 by the authors. 2017 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s17020414 ;PMID: 28230767

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  • Title:
    An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox
  • Author: Jing, Luyang ; Wang, Taiyong ; Zhao, Ming ; Wang, Peng
  • Subjects: Artificial neural networks ; Back propagation ; Back propagation networks ; Damage detection ; Data fusion ; Data integration ; deep convolutional neural networks ; Fault diagnosis ; Feature extraction ; feature learning ; Gearboxes ; Mechanical systems ; multi-sensor data fusion ; Multisensor fusion ; Neural networks ; Optimization ; Planet detection ; planetary gearbox ; Sensors ; Support vector machines ; Tasks
  • Is Part Of: Sensors (Basel, Switzerland), 2017-02, Vol.17 (2), p.414-414
  • Description: A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s17020414
    PMID: 28230767
  • Source: DOAJ Directory of Open Access Journals
    GFMER Free Medical Journals
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
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