基于LabVIEW的电动机轴承故障诊断和性能退化评估系统设计

Design of Assessment System of Electric Motor Bearing Fault Diagnosis and Performance Degradation Based on LabVIEW

轴承作为电动机的核心部件,主要起到支撑引导轴、减小设备摩擦、连接不同设备等作用,准确判断其故障类型并评估其健康状态对于合理安排设备的检修具有重大意义。为此,设计了一套基于LabVIEW平台的电动机轴承实时故障诊断和性能退化评估系统。利用卷积神经网络(CNN)的特征挖掘能力,自主学习原始振动信号中的故障特征,在LabVIEW平台上构建故障诊断模型,实现轴承运行状态的实时诊断;对原始振动信号小波降噪后,提取信号时域特征,通过对所提取的特征进行主元分析(PCA)来获取表征轴承性能退化的综合指标;在LabVIEW平台上开发电动机轴承的故障诊断与性能退化评估系统软件。在线故障诊断和性能评估实验结果验证了该系统的实时性和有效性。

As a pivotal component of electric motors,bearings primarily function to support and guide shafts,reduce equipment friction,and facilitate the connection of different devices.Accurate identification of bearing failure types and assessment of their health status are of significant importance for the rational scheduling of equipment maintenance.This study devises a real-time fault diagnosis and performance degradation assessment system for electric motor bearings based on the LabVIEW virtual instrument platform.Firstly,by leveraging the feature extraction capability of convolutional neural networks(CNN),the system autonomously learns fault features from raw vibration signals,constructs a fault diagnostic model on the LabVIEW platform,and achieves real-time diagnosis of bearing operating conditions.Secondly,by applying wavelet denoising to the raw vibration signal and extracting their time-domain features,a comprehensive indicator representing bearing performance degradation is obtained using principal component analysis(PCA).The software for the fault diagnosis and performance degradation assessment system for electric motor bearings is developed on the LabVIEW platform.Experimental results of online fault diagnosis and performance assessment validate the real-time effectiveness and efficiency of the proposed system.