摘要
电力监控系统是机电工程安全、高效运行的核心保障,但其在复杂工况下的故障诊断与优化面临多源数据耦合、动态适应性不足等挑战。基于此,文章概述了电力监控系统的数据特征和故障模式,提出一种基于多源数据融合的动态协同诊断与优化方法,构建贝叶斯概率模型实现故障定位,利用线性回归模型优化系统响应,形成融合数据特征分析与模型参数调优的一体化解决方案,旨在提高故障诊断精度与系统响应能力,为机电工程电力监控的可靠性优化提供技术支撑。
The power monitoring system is the core guar-antee for the safe and efficient operation of electrome-chanical engineering.However,its fault diagnosis and optimization under complex working conditions are faced with challenges such as multi-source data coupling and insufficient dynamic adaptability.Based on this,this paper outlines the data features and fault modes of the power monitoring system,and proposes a dynamic collaborative diagnosis and optimization method based on multi-source data fusion.The Bayesian probability model is built to re-alize fault positioning,the linear regression model is used to optimize the system response,and an integrated solution that fuses data feature analysis and model parameter op-timization is formed,aiming to improve the accuracy of fault diagnosis and system response and provide technical support for the reliability optimization of power monitor-ing in electromechanical engineering.
作者
宗德齐
ZONG Deqi(Shanghai Lanke Environmental Technology Co.,Ltd.,Shanghai 201206,China)
出处
《光源与照明》
2025年第6期87-89,共3页
Lamps & Lighting
关键词
机电工程
电力监控系统
故障诊断
贝叶斯概率模型
线性回归模型
electromechanical engineering
power moni-toring system
fault diagnosis
Bayesian probability model
linear regression model
