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

Zhuo Zhang, Wen Jiang, Jie Geng, Xinyang Deng, Xiang Li
2020 IEEE Access  
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-negative sparse constrained deep neural networks (NSCDNN) and Dempster-Shafer theory (DST) is proposed in this paper. The deep neural network is trained by non-negative constraint and sparse constraint,
more » ... arse constraint, which can learn part-based representation of fault data. The improved DST is combined with the classification confidence and accuracy of NSCDNN model, which can deal with the uncertainty of information from different sensors. Experimental results of the data provided by Case Western Reserve University Bearing Data Center show that the proposed NSCDNN-DST algorithm can improve the accuracy of fault diagnosis effectively. INDEX TERMS Fault diagnosis, deep learning, sparse autoencoders, Dempster-Shafer theory, sensors fusion. 18182 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ VOLUME 8, 2020
doi:10.1109/access.2020.2966260 fatcat:36nfkyzjcbefthxdci5ta62c4m