用户名 密 码 登 录 忘记密码 免费注册

网站会员中心 | 设为首页 | 加入收藏

 您好，欢迎光临中国轴承行业网         退出登陆

会员中心 | 设为首页 | 加入收藏

协会

统计

国外文献 > 1.轴承 > 8. 系统与应用 > 电机与旋转机械

关键词搜索：

标 题： 3-phase Induction Motor Bearing Fault Detection and Isolation using MCSA Technique based on Neural Network Algorithm
基于神经网络算法的MCSA技术用于三相感应电机轴承故障检测与隔离
作 者： Nawal A. Hussein
Dhari Yousif Mahmood
Isam Mahmood Abdulbaqi
关键词： Induction motors  [感应电动机]
Diagnosis  [ 诊断]
Data acquisition  [ 数据采集]
Fault detection  [ 故障检测]
Modeling  [ 建模]
Bearings fault  [ 轴承故障]
出版年： 2016
期刊名： Advances in applied computational mechanics
应用计算力学的进展
年卷期： 2016, vol.7, no.2
摘 要： This paper shows a system that has the ability to diagnose bearing fault in three phase induction motor by using Motor Current Signature Analysis (MCSA) technique associated with artificial neural network (ANN) algorithm. Mathematical models for healthy and faulty conditions built to demonstrate theoretically the behavior of 3-phase induction motor in both cases. The effects of such a fault on motor currents waveforms at different loads studied experimentally using practical data acquisition and Fast Fourier Transform (FFT) analysis. The harmonic content for this fault current, through the loading range, is studied, and fed to neural network algorithm. A numerical optimization technique using Levenberg-Marquardt algorithm has been done for ANN training and testing. This system prepared to be used in industrial applications to diagnose and isolate the faulty motors immediately at their incipient stage, and to avoid any damage occur for the motors, or for their supply system.

本文提出了一种利用电机电流特征分析（MCSA）技术结合人工神经网络（ANN）算法，用于诊断三相感应电动机轴承故障的系统。通过建立健康状态和故障状态下的数学模型，从理论上展示了三相感应电动机在两种情况下的工作行为。该系统通过实际数据采集与快速傅里叶变换（FFT）分析，对不同负载下故障对电机电流波形的影响进行了实验研究。同时，分析了故障电流在整个负载范围内产生的谐波成分，并将其输入神经网络算法进行处理。采用Levenberg-Marquardt算法的数值优化方法对ANN的训练与测试进行了实施。该系统可应用于工业领域，实现对故障电机在初期阶段的及时诊断与隔离，从而避免电机或其供电系统遭受任何损坏。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

VIP网员

联系我们 | 登录注册 | 会员管理 | 网员服务 | 服务信息 | 广告服务 | 高薪聘请 | 法律声明 | 关于我们

京公网安备 11010202009788    京ICP备2021015424号