Acoustic fault diagnosis of three-phase induction motors using smartphone and deep learning

被引：5

作者：

Glowacz, Adam ^{[1
]}

Sulowicz, Maciej ^{[2
]}

Zielonka, Jakub ^{[2
]}

Li, Zhixiong ^{[3
]}

Glowacz, Witold ^{[1
]}

Kumar, Anil ^{[4
]}

机构：

[1] AGH Univ Krakow, Fac Elect Engn Automat Comp Sci & Biomed Engn, Dept Automat Control & Robot, Al A Mickiewicza 30, PL-30059 Krakow, Poland

[2] Cracow Univ Technol, Fac Elect & Comp Engn, Dept Elect Engn, PL-31155 Krakow, Poland

[3] Opole Univ Technol, Dept Mfg Engn & Automat Prod, PL-45758 Opole, Poland

[4] Wenzhou Univ, Coll Mech & Elect Engn, Wenzhou, Peoples R China

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2025年 / 262卷

关键词：

Acoustic analysis; Induction motor; Fault diagnosis; Deep learning;

D O I：

10.1016/j.eswa.2024.125633

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Faults in induction motors can halt production lines in factories, leading to downtime and resulting in production and economic losses. Therefore, it is crucial to ensure that motors operate reliably. This paper describes an approach for the acoustic fault diagnosis of rotor bars in three-phase induction motors (IM). The authors analyzed the following conditions: a healthy IM, an IM with one broken rotor bar, an IM with two broken rotor bars, and an IM with three broken rotor bars. The FFT method was used to compute the FFT spectrum of the acoustic signals. An original feature extraction method DWV (Differences of Word Vectors) was proposed to compute the acoustic features. DenseNet-201, ResNet-18, ResNet-50, and EfficientNet-b0 were used to classify these acoustic features. The computed recognition efficiency is 100 %. The proposed method was also verified using a low-pass filter of 1-1225 Hz and word coding.

引用

页数：8

共 30 条

[1] Multi-input CNN based vibro-acoustic fusion for accurate fault diagnosis of induction motor [J].