FAULT DETECTION IN INDUCTION MOTORS USING VIBRATION PATTERNS AND ELM NEURAL NETWORK

被引：0

作者：

Ramalho, G. L. B. ^{[1
]}

Pereira, A. H. ^{[1
]}

Reboucas Filho, P. P. ^{[1
]}

Medeiros, C. M. S. ^{[1
]}

机构：

[1] Inst Fed Ceara, Limoeiro Do Norte, Brazil

来源：

HOLOS | 2014年 / 30卷 / 04期

关键词：

Fault detection; MEMS sensors; ELM neural network;

D O I：

10.15628/holos.2014.1925

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The condition monitoring of industrial electric motors provides information to help planning maintenance interventions before the occurrence of failures. This paper proposes a new approach for monitoring the operational condition of three-phase induction motors based on the extraction of characteristics of a signal obtained with MEMS accelerometers. The data extracted from the decomposition of the vibration signal using Haar Transform and the fractal dimension are used to train a ELM neural network. The results of our experiments demonstrated the feasibility of the proposed methodology in detection and identification of mechanical and electrical failures

引用

页码：185 / 194

页数：10

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