Quantitative Modeling and Simulation for Stator Inter-turn Fault Detection in Industrial Machine

被引：5

作者：

Verma, Amar Kumar ^{[1
]}

Spandana, P. ^{[1
]}

Padmanabhan, S., V ^{[1
]}

Radhika, Sudha ^{[1
]}

机构：

[1] BITS Pilani, Dept Elect & Elect Engn, Hyderabad Campus, Hyderabad 500078, India

来源：

INTELLIGENT COMPUTING AND COMMUNICATION, ICICC 2019 | 2020年 / 1034卷

关键词：

Fault detection and diagnosis (FDD); Motor current signature analysis (MCSA); Artificial intelligent; Condition monitoring (CM);

D O I：

10.1007/978-981-15-1084-7_10

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper deals with quantitative modeling and simulation for stator inter-turn fault detection in industrial machine. Quantitative modeling and simulation for both healthy and faulty induction machine at different loading conditions are studied and validated with experimental results under same operating condition. Fault identification techniques are mainly categorized into signature extractionbased, model-based, and knowledge-based approach. Introduction of statistical feature extraction, feature selection, and fault classification using knowledge-based approach are intelligent methodology for stator inter-turn fault identification and diagnosis for industrial machine.

引用

页码：87 / 97

页数：11

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