An advanced neural-network-based instrument fault detection and isolation scheme

被引：38

作者：

Betta, G ^{[1
]}

Liguori, C

Pietrosanto, A

机构：

[1] Univ Cassino, Dept Ind Engn, I-03043 Cassino, FR, Italy

[2] Univ Salerno, Dept Informat Engn & Elect Engn, I-84084 Fisciano, SA, Italy

来源：

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | 1998年 / 47卷 / 02期

关键词：

artificial neural network; expert systems; fault detection; induction motor testing; instrument fault location;

D O I：

10.1109/19.744199

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 [电气工程]; 0809 [电子科学与技术];

摘要：

An advanced scheme for instrument fault detection and isolation is proposed. It is based on artificial neural networks (ANN's), organized in layers and handled by knowledge-based analytical redundancy relationships, ANN design and training is performed by genetic algorithms which allow ANN architecture and parameters to be easily optimized. The diagnostic performance of the proposed scheme is evaluated vith reference to a measurement station for automatic testing of induction motors.

引用

页码：507 / 512

页数：6

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