面向造纸污水处理的故障诊断复合算法研究
Research on Fault Diagnosis Composite Algorithm for Papermaking Wastewater Treatment
戴静 1陈江萍 1成兰 1刘冬1
作者信息
- 1. 陕西服装工程学院,陕西 西安,712046
- 折叠
摘要
故障诊断是保障系统稳定性与安全性的关键节点.在造纸污水处理过程中,系统硬件设备由于长期处于恶劣环境极易引发系统故障,因此准确诊断故障以避免不可挽回的损失至关重要.基于此,针对造纸污水处理过程的特点,以主成分分析技术提取故障主元而明确故障诊断模型输入量,以粒子群优化算法优化机器学习算法支持向量机而构成故障诊断复合算法,由此搭建了面向造纸污水处理的故障诊断模型,并进行了仿真分析.结果发现,面向造纸污水处理的故障诊断复合算法正确率可达 96.9%,且稳定性与鲁棒性较高,可广泛推广至多工业污水处理领域.
Abstract
Fault diagnosis is a key node in ensuring system stability and safety.In the process of papermaking wastewater treatment,the hardware equipment of the system is prone to system failures due to long-term exposure to harsh environments.Therefore,accurate fault diagnosis is crucial to avoid irreversible losses.This article focuses on the characteristics of the papermaking wastewater treatment process.Principal component analysis technology is used to extract fault principal components and clarify the input of the fault diagnosis model.The particle swarm optimization algorithm is improved to optimize the machine learning algorithm support vector machine to form a fault diagnosis composite algorithm.Based on this,a fault diagnosis model for papermaking wastewater treatment is constructed and simulated.The results show that the accuracy of the fault diagnosis composite algorithm for papermaking wastewater treatment can reach 96.9%,and it has high stability and robustness,which can be widely promoted in the field of multi industrial wastewater treatment.
关键词
造纸污水/污水处理/故障诊断/粒子群优化算法/支持向量机Key words
papermaking wastewater/wastewater treatment/fault diagnosis/improve particle swarm optimization algorithm/support vector machine引用本文复制引用
基金项目
陕西服装工程学院横向科研项目(2023sfh0140)
出版年
2024
NETL
NSTL
万方数据