摘要
污水处理系统是一个复杂的非线性大系统,存在作业环境恶劣、控制目标复杂等问题。这些问题导致污水厂故障频发,因此急需开发高效的监测技术。本研究提出了一种新的故障监测技术,即预测元-相关向量机方法。该方法是将可预测元算法与相关向量机进行有机结合。首先利用可预测元算法对在污水厂采集的数据进行特征提取,去除重复特征和冗余信息。然后,利用处理后的数据训练相关向量机模型。为了验证所提方法的优越性,将预测元-相关向量机与相关向量机(RVM)、主元分析-相关向量机(PCA-RVM)和独立元分析-相关向量机(ICA-RVM)3种方法同时用于监测国际水协会提供的污水仿真基准平台(BSM1)。实验表明本研究所提方法诊断精度高于3种基础方法。
As a complex nonlinear large-scale system,wastewater treatment plant(WWTP)faces problems such as bad working environment,complex control objectives.These problems often lead to fault of the system,so it is urgent to develop the efficient monitoring technology.This study proposed a new fault detection technology,namely,the forecast component-relevance vector machine,which combines the relevance vector machine with the forecastable component analysis.Firstly,the forecastable component algorithm is used to extract features information from the collected data of WWTPs,in order to remove duplicate features and redundant information.Then the rele-vance vector machine model is trained by offline data.In order to verify the superiority of the proposed method,the forecast component-relevance vector machine and another three methods(RVM,PCA-RVM,ICA-RVM)are used to monitor the wastewater treatment Benchmark Simulation Model 1(BSM1)platform provided by the International Water Association.Experiments show that the fault detection accuracy of the forecast component-relevance vector machine is higher than the other three methods.
作者
程洪超
吴菁
刘乙奇
黄道平
CHENG Hongchao;WU Jing;LIU Yiqi;HUANG Daoping(School of Automation Science and Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China)
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2020年第3期10-17,共8页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(61873096,61673181)
广州市科技计划项目(201804010256)。
关键词
污水处理
故障诊断
相关向量机
可预测元分析
特征提取
wastewater treatment
fault detection
relevance vector machine
forecastable component analysis
feature extraction
