基于优化BP神经网络的冷水机组故障监测研究
Research on Fault Monitoring of Chillers Unit Based on Optimized BP Neural Network
郑力达 1张玉成1
作者信息
摘要
为提升冷水机组故障监测的准确性和实时性,结合粒子群优化算法并引入动量因子,提出一种基于优化BP神经网络的实时在线监测模型.主要介绍了BP神经网络的优化方式、冷水机组故障分析及参数选取、在线状态管理,通过Java编程语言和Encog神经网络库构建BP神经网络模型,并部署于某城市智慧运维平台,该模型有效提升了平台对冷水机组故障的监测能力.
Abstract
In order to improve the accuracy and real-time performance of chiller failure monitoring,a real-time online monitoring model based on optimized BP neural network is proposed by combining the particle swarm optimization algorithm and the introduction of momentum factor.The optimization method of BP neural network,chiller fault analysis and parameter selection,and online state management are mainly introduced,and the BP neural network model is constructed by Java programming language and Encog neural network library and deployed in a city's intelligent operation and maintenance platform,which effectively improves the platform's ability to monitor chiller faults.
关键词
BP神经网络/冷水机组/粒子群算法/动量因子/在线监测Key words
BP neural network/chillers unit/particle swarm optimization/momentum factor/online monitoring引用本文复制引用
出版年
2024
NETL
NSTL
万方数据