摘要
电机烧损是工业生产中常见的故障类型,主要由过载、电气与机械故障等因素引起。这类故障不仅导致设备频繁维修,还可能造成生产中断和经济损失。为解决传统预防方法存在的诊断滞后和效率低下问题,提出一种基于随机森林算法的智能化预防模型:通过分析电机运行数据,结合特征选择和模型训练,实现了电机烧损的高效预测和动态负荷优化。实验结果表明,该模型能显著提升电机烧损预测的准确性和可靠性,在多维度数据分析方面也有较强优势。相关研究为电机智能化管理提供了创新路径,也为工业设备的维护优化提供了参考。
Motor burnout is a common fault in industrial production,primarily caused by factors such as overload,electrical and mechanical failures.It leads to not only frequent equipment maintenance but also production downtime and economic losses.To address the delay and the inefficiencies of detecting faults,which are the common problems of traditional prevention,this paper proposed an intelligent prevention model based on the random forest algorithm.By conducting motor operating data analysis,together with feature selection and model training,motor burnout was predicted efficiently,and motor load was optimized dynamically.Experimental results show that the proposed model significantly enhances the accuracy and reliability of prediction,exhibiting superior performance in multidimensional data analysis.This research provides not only an innovative approach to the intelligent management of motors but also valuable insights for the optimization of industrial equipment maintenance.
作者
周恩思
贾春娜
ZHOU Ensi;JIA Chunna(Shanghai Lanmo Industrial Automation Technology Center,Shanghai 201319,China;A.yite Technology Co.,Ltd.,Shanghai 201319,China;Huadian Aero Turbine Service Co.,Ltd,Shanghai 201108,China)
出处
《江苏工程职业技术学院学报》
2025年第2期16-21,共6页
Journal of Jiangsu College of Engineering and Technology
关键词
随机森林算法
电机烧损
故障预测
动态负荷优化
智能化预防
random forest algorithm
motor burnout
fault prediction
dynamic load optimization
intelligent prevention
