摘要
为能够快速、准确地对制粉系统故障进行诊断,根据制粉系统的运行特性和故障特征,对基于非线性状态估计的制粉系统故障诊断方法,提出了新的构造过程记忆矩阵的方法,依据马氏距离对故障数据样本进行预处理,并使用预处理后的数据构造过程记忆矩阵,从而有效地减少了冗余数据,提高了诊断效率和诊断的实用性和稳定性。
To diagnose the faults occurred in pulverizing system accurately and rapidly,this paper proposed a novel method for constructing the process memory matrix,against the pulverizing system fault diagnosis method based on nonlinear state estimation,according to the operation characteristics and fault features of the coal milling system.This new method is used to pretreat fault data based on Mahalanobis distance.In addition,it establishes process memory matrix based on the pretreated fault data to reduce redundancy data.By this way,the new method improves the fault diagnosis efficiency and plays a more active role in ensuring practicability and stability of the coal milling system fault diagnosis.
出处
《热力发电》
CAS
北大核心
2015年第12期87-92,97,共7页
Thermal Power Generation
基金
教育部留学回国人员科研启动基金资助项目(改进型非线性状态估计的制粉系统故障诊断)
关键词
制粉系统
故障诊断
非线性
状态估计
马氏距离
过程记忆矩阵
冗余数据
诊断效率
coal milling system
fault diagnosis
nonlinear
state estimation
Mahalanobis distance
process memory matrix
redundant data
diagnosis efficiency
