Gear and motor fault modeling and detection based on motor current analysis

N. Feki; G.Clerc; Ph.Velex

首页> 外文期刊>Electric power systems research >Gear and motor fault modeling and detection based on motor current analysis

【24h】

Gear and motor fault modeling and detection based on motor current analysis

机译：基于电动机电流分析的齿轮和电动机故障建模与检测

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

This paper deals with the simulation of the dynamic behavior of induction motor/gear systems. The corresponding model combines: (ⅰ) a permeance network aimed at representing squirrel-cage induction motors and (ⅱ) a 3D dynamic model of spur and helical gears including the shafts and bearings. Some classic motor defects (rotor eccentricity) and gear faults (tooth spalls) are considered and an iterative solution technique is presented which makes it possible to solve the coupled electro-mechanical problem. Based on a number of simulations, it is shown that the frequencies associated with defects can be identified and that distant detection and monitoring of tooth faults using stator currents seem promising alternatives to the classic techniques based on mechanical vibration analysis.

机译：本文涉及感应电动机/齿轮系统动态行为的仿真。相应的模型包括：（ⅰ）旨在代表鼠笼式感应电动机的磁导网络，以及（ⅱ）正齿轮和斜齿轮（包括轴和轴承）的3D动态模型。考虑了一些经典的电动机缺陷（转子偏心距）和齿轮故障（齿裂），并提出了一种迭代求解技术，可以解决耦合的机电问题。基于大量仿真，结果表明，可以识别与缺陷相关的频率，并且使用定子电流对牙齿故障进行远程检测和监视似乎是基于机械振动分析的经典技术的有前途的替代方法。

著录项

来源
《Electric power systems research》 |2013年第2期|28-37|共10页
作者
N. Feki; G.Clerc; Ph.Velex;
展开▼
作者单位

University de Lyon - INSA Lyon - LaMCoS - UMR CNRS 5259, Batimentjean d'Alembert - 20, Avenue Albert Einstein, 69621 Villeurbanne Cedex, France;

University de Lyon - Universite Lyon 1 - Ampere - UMR CNRS 5005, Batiment Omega - 43 Boulevard du 11 Novembre 1918,69622 Villeurbanne Cedex, France;

University de Lyon - INSA Lyon - LaMCoS - UMR CNRS 5259, Batimentjean d'Alembert - 20, Avenue Albert Einstein, 69621 Villeurbanne Cedex, France;

展开▼
收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
fault diagnosis; gear; induction machine; motor current signature analysis; permeance network;

机译：故障诊断;齿轮;感应机电机电流签名分析;磁导网络;

相似文献

外文文献
中文文献
专利

1. MOTOR CURRENT SIGNATURE ANALYSIS AND NEGATI VESEQUENCE CURRENT BASED STATOR WINDING SHORT FAULTDETECTION IN AN INDUCTION MOTOR [J] . SILVIA OVIEDO, JABID EDUARDO QUIROGA MéNDEZ, CARLOS BORRáS Dyna . 2011,第170期

机译：感应电动机中基于电动机电流签名分析和负矢量电流的定子绕组短路故障检测
2. Current/Voltage-Based Detection of Faults in Gears Coupled to Electric Motors [J] . Rajagopalan S., Habetler T. G., Harley R. G., IEEE Transactions on Industry Applications . 2006,第期

机译：基于电流/电压的齿轮故障检测
3. Fault Detection and Diagnosis of Induction Motors Using Motor Current Signature Analysis and a Hybrid FMM–CART Model [J] . Seera M., Lim C. P., Ishak D., Neural Networks and Learning Systems, IEEE Transactions on . 2012,第1期

机译：基于电动机电流签名分析和FMM-CART混合模型的感应电动机故障检测与诊断
4. Gear and bearings fault detection using motor current signature analysis [C] . Ndeye Gueye Lo, Abdenour Soualhi, Marouane Frinì, IEEE Conference on Industrial Electronics and Applications . 2018

机译：利用电动机电流信号分析检测齿轮和轴承故障
5. Separating load torque oscillation and rotor faults in stator current based-induction motor condition monitoring [D] . Wu, Long 2007

机译：在基于定子电流的感应电动机状态监测中分离负载转矩振荡和转子故障
6. Detection and classification of bearing faults in industrial geared motors using temporal features and adaptive neuro-fuzzy inference system [O] . Choug Abdelkrim, Mohamed Salah Meridjet, Nadir Boutasseta, 2019

机译：基于时间特征和自适应神经模糊推理系统的工业齿轮电动机轴承故障检测与分类
7. Current Based Fault Detection and Diagnosis of Induction Motors. Adaptive Mixed-Residual Approach for Fault Detection and Diagnosis of Rotor, Stator, Bearing and Air-Gap Faults in Induction Motors Using a Fuzzy Logic Classifier with Voltage and Current Measurement only. [O] . Bradley William John 2013

机译：基于电流的感应电动机故障检测和诊断。仅使用电压和电流测量的模糊逻辑分类器的自适应混合残差方法对异步电动机的转子，定子，轴承和气隙故障进行故障检测和诊断。

Gear and motor fault modeling and detection based on motor current analysis

摘要

著录项

相似文献

相关主题

期刊订阅