首页|基于谐波诊断技术的电机故障在线监测系统

基于谐波诊断技术的电机故障在线监测系统

On-line Monitoring System of Motor Fault Based on Harmonic Diagnosis Technology

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目的 为解决振动法、电流法等传统故障诊断方法在进行电机检测时,存在检测不全面、时效性差及诊断结果难解读等问题,方法提出一种基于谐波诊断技术的电机故障诊断系统,结合谐波诊断技术、物联网技术和智能诊断算法,实现电机运行健康状态的在线监测.搭建电机实验平台,对比分析振动法、电流法和谐波法的准确性和全面性,并对电机轴承、线圈、联轴器等部位进行不同程度的老化和破坏,验证诊断结果的准确性、时效性和全面性.结果 以谐波诊断技术为基础的监测系统,在电机各个部位发生不同程度的老化和破坏时,与其他两种方法相比电机轴承、线圈、联轴器等八个部位的劣化值均发生了与实际情况较为符合的变化,诊断结果仅需1s左右,同时提供故障原因和维修建议.结论 基于谐波诊断技术的电机故障在线监测系统响应速度快、检测全面、诊断报告详细且简洁易懂,为电机故障诊断技术的发展提供了新的参考方向..
Objective In order to solve the problems of incomplete detection,poor timeliness and difficult interpre-tation of diagnosis results in motor detection by traditional fault diagnosis methods such as vibration method and current method.Methods The motor fault diagnosis system based on harmonic diagnosis technology was proposed,combined with the harmonic diagnosis technology,internet of things technology and intelligent diagnosis algo-rithm,to realize online monitoring of motor running health status.A motor experiment platform was built to com-pare and analyze the accuracy and comprehensiveness of the vibration method,current method and harmonic method.The different degrees of aging and damage were carried out on motor bearings,coils,couplings and other parts to verify the accuracy,timeliness and comprehensiveness of diagnosis results.Results The deterioration val-ues of eight parts of the motor bearing,coil,coupling,etc.changed in line with the actual situation,when different degrees of aging and damage occured in various parts of the motor for the monitoring system based on the harmon-ic diagnosis technology.To ger the diagnosis result took about 1 s only,with the fault causes and maintenance sug-gestions provided.Conclusion The on-line motor fault monitoring system based on the harmonic diagnosis technol-ogy has fast response speed,comprehensive detection,detailed and simple diagnosis report,which provides a new reference direction for the development of motor fault diagnosis technology.

motor fault diagnosisharmonic diagnosis technologyInternet of things technologyOn-line monitoring

代斌、胡业林、宋晓、赵宝、张佳璐

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安徽理工大学电气与信息工程学院,安徽 淮南 232001

安徽理工大学计算机科学与工程学院,安徽 淮南 232001

电机故障诊断 谐波诊断技术 物联网技术 在线监测

2024

安徽理工大学学报(自然科学版)
安徽理工大学

安徽理工大学学报(自然科学版)

影响因子:0.331
ISSN:1672-1098
年,卷(期):2024.44(3)
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