Advances in Machine Learning for Sensing and Condition Monitoring

被引：5

作者：

Ao, Sio-Iong ^{[1
]}

Gelman, Len ^{[2
]}

Karimi, Hamid Reza ^{[3
]}

Tiboni, Monica ^{[4
]}

机构：

[1] Int Assoc Engineers, Unit 1 1 F, Hong Kong, Peoples R China

[2] Univ Huddersfield, Ctr Efficiency & Performance Engn, Huddersfield HD1 3DH, England

[3] Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy

[4] Univ Brescia, Dept Mech & Ind Engn, I-25123 Brescia, Italy

来源：

APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 23期

关键词：

machine learning deep learning; sensing; condition monitoring; CONVOLUTIONAL NEURAL-NETWORK; FAULT-DIAGNOSIS; AUTOENCODER; ALGORITHM; FRAMEWORK; SENSORS;

D O I：

10.3390/app122312392

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

In order to overcome the complexities encountered in sensing devices with data collection, transmission, storage and analysis toward condition monitoring, estimation and control system purposes, machine learning algorithms have gained popularity to analyze and interpret big sensory data in modern industry. This paper put forward a comprehensive survey on the advances in the technology of machine learning algorithms and their most recent applications in the sensing and condition monitoring fields. Current case studies of developing tailor-made data mining and deep learning algorithms from practical aspects are carefully selected and discussed. The characteristics and contributions of these algorithms to the sensing and monitoring fields are elaborated.

引用

页数：23

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