Fault Diagnosis in Direct Current Electric Motors via an Artificial Neural Network

被引：2

作者：

Aravanis, Theofanis I. ^{[1
]}

Aravanis, Tryfon-Chrysovalantis I. ^{[2
]}

Papadopoulos, Polydoros N. ^{[3
]}

机构：

[1] Univ Patras, Dept Business Adm, Patras, Greece

[2] Univ Patras, Dept Mech Engn & Aeronaut, Stochast Mech Syst & Automat SMSA Lab, Patras, Greece

[3] TEI Western Greece, Dept Mech Engn TE, Patras, Greece

来源：

ENGINEERING APPLICATIONS OF NEURAL NETWORKSX | 2019年 / 1000卷

关键词：

Direct current electric motors; Artificial neural networks; Artificial intelligence; Fault detection and classification;

D O I：

10.1007/978-3-030-20257-6_42

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The combined problem of fault detection and classification (referred to as fault diagnosis) of Direct Current (DC) electric motors via a simple, yet powerful, technique based on an Artificial Neural Network (ANN) is proposed. The ability of an ANN in identifying patterns with high fidelity-without the need of any rigorous mathematical model of the system under investigation-leads to an excellent diagnosis performance, even for faults that result in almost indistinguishable output system responses (both in time and in frequency domain). The flexibility and speed of the presented method indicate that it can easily be applied to on-line fault diagnosis as well.

引用

页码：488 / 498

页数：11

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