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作者:

Li, Huifang (Li, Huifang.) | Hu, Guangzheng (Hu, Guangzheng.) | Li, Jianqiang (Li, Jianqiang.) | Zhou, Mengchu (Zhou, Mengchu.)

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EI Scopus SCIE

摘要:

Recently, deep neural network (DNN) models work incredibly well, and edge computing has achieved great success in real-world scenarios, such as fault diagnosis for large-scale rotational machinery. However, DNN training takes a long time due to its complex calculation, which makes it difficult to optimize and retrain models. To address such an issue, this work proposes a novel fault diagnosis model by combining binarized DNNs (BDNNs) with improved random forests (RFs). First, a BDNN-based feature extraction method with binary weights and activations in a training process is designed to reduce the model runtime without losing the accuracy of feature extraction. Its generated features are used to train an RF-based fault classifier to relieve the information loss caused by binarization. Second, considering the possible classification accuracy reduction resulting from those very similar binarized features of two instances with different classes, we replace a Gini index with ReliefF as the attribute evaluation measure in training RFs to further enhance the separability of fault features extracted by BDNN and accordingly improve the fault identification accuracy. Third, an edge computing-based fault diagnosis mode is proposed to increase diagnostic efficiency, where our diagnosis model is deployed distributedly on a number of edge nodes close to the end rotational machines in distinct locations. Extensive experiments are conducted to validate the proposed method on the data sets from rolling element bearings, and the results demonstrate that, in almost all cases, its diagnostic accuracy is competitive to the state-of-the-art DNNs and even higher due to a form of regularization in some cases. Benefited from the relatively lower computing and storage requirements of BDNNs, it is easy to be deployed on edge nodes to realize real-time fault diagnosis concurrently.

关键词:

Binarized deep neural networks (BDNNs) deep learning (DL) random forests (RFs) Edge computing Feature extraction Artificial intelligence intelligent fault diagnosis Fault diagnosis Real-time systems Radio frequency Industrial Internet of Things (IoT) edge computing Computational modeling Training

作者机构:

  • [ 1 ] [Li, Huifang]Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
  • [ 2 ] [Hu, Guangzheng]Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
  • [ 3 ] [Li, Jianqiang]Beijing Univ Technol, Sch Software Engn, Beijing 100024, Peoples R China
  • [ 4 ] [Zhou, Mengchu]New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA
  • [ 5 ] [Zhou, Mengchu]St Petersburg State Marine Tech Univ, Dept Cyber Phys Syst, St Petersburg 198262, Russia

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来源 :

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

ISSN: 1545-5955

年份: 2021

期: 2

卷: 19

页码: 1109-1119

5 . 6 0 0

JCR@2022

ESI学科: ENGINEERING;

ESI高被引阀值:87

JCR分区:1

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 86

ESI高被引论文在榜: 0 展开所有

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