Vibration Anomaly Detection using Deep Autoencoders for Smart Factory

被引：3

作者：

Waters, Mark ^{[1
]}

Waszczuk, Pawel ^{[1
]}

Ayre, Rodney ^{[2
]}

Dreze, Alain ^{[2
]}

McGlinchey, Don ^{[1
]}

Alkali, Babakalli ^{[1
]}

Morison, Gordon ^{[1
]}

机构：

[1] Glasgow Caledonian Univ, Sch Comp Engn & Built Environm, 70 Cowcaddens Rd, Glasgow G4 0BA, Lanark, Scotland

[2] Mitsubishi Elect Air Conditioning Syst Europe LTD, Houston Ind Estate, Livingston EH54 5EQ, Scotland

来源：

2022 IEEE SENSORS | 2022年

基金：

“创新英国”项目;

关键词：

IIoT; Smart Factory; Condition Monitoring; Autoencoder; Induction Motor; Artificial Intelligence; MOTOR FAULT-DETECTION; DIAGNOSIS;

D O I：

10.1109/SENSORS52175.2022.9967320

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Early fault detection in production is crucial for manufacturing facilities to prevent unplanned downtimes and maximise the operational life of equipment. The aim of this paper is to present a method of anomaly detection for an inservice motor using self-supervised learning. The authors have developed a condition monitoring system for a Smart Factory using deep autoencoders. The system was installed in a live production facility with the goal of improving site maintenance.

引用

页数：4

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