Fault Detection in Soft-started Induction Motors using Convolutional Neural Network Enhanced by Data Augmentation Techniques

被引：2

作者：

Pasqualotto, Dario ^{[1
]}

Navarro Navarro, Angela ^{[2
]}

Zigliotto, Mauro ^{[1
]}

Antonino-Daviu, Jose A. ^{[3
]}

Biot-Monterde, Vicente ^{[2
]}

机构：

[1] Univ Padua, Dept Management & Engn, Vicenza, Italy

[2] Univ Politecn Valencia, Dept Ingn Elect, Valencia, Spain

[3] Univ Politecn Valencia, Inst Tecnol Energia, Valencia, Spain

来源：

IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY | 2021年

关键词：

Convolutional Neural Networks; Data Augmentation; Induction Motor; Soft-starter; Stray Flux; ROTOR FAULTS;

D O I：

10.1109/IECON48115.2021.9589439

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Stray flux analysis is an interesting source of information for the diagnosis of Induction Motors (IMs). The widespread use of these motors in industry leads to a necessity of additional tools and methods for their predictive maintenance. On the other hand, soft-starters are increasingly used to reduce the high consumption of IMs at start-up. In this work, AI techniques based on convolutional neural networks are applied to detect rotor faults in soft-started motors. The objective is the automatic early detection of broken bars, avoiding the necessity of user intervention to interpret the obtained results. This work proves the potential of the methodology, including a successful set of experimental results.

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页数：6

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