Fault Diagnosis Based on Non-Negative Sparse Constrained Deep Neural Networks and Dempster–Shafer Theory

Fault Diagnosis Based on Non-Negative Sparse Constrained Deep Neural Networks and Dempster–Shafer Theory

Fault diagnosis is an important technology to ensure the safe and reliable operation of equipment. Deep learning driven by big data brings new opportunities for fault diagnosis. Due to the diversity and complexity of the actual fault data distribution, a fault diagnosis algorithm based on non-negati...

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Bibliographic Details
Main Authors: Zhuo Zhang, Wen Jiang, Jie Geng, Xinyang Deng, Xiang Li
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Fault diagnosis
deep learning
sparse autoencoders
Dempster–Shafer theory
sensors fusion
Online Access:https://ieeexplore.ieee.org/document/8957475/

Internet

https://ieeexplore.ieee.org/document/8957475/

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