Expectation-Maximization Approach to Fault Diagnosis With Missing Data

被引：69

作者：

Zhang, Kangkang ^{[1
,2
]}

Gonzalez, Ruben ^{[2
]}

Huang, Biao ^{[2
]}

Ji, Guoli ^{[1
]}

机构：

[1] Xiamen Univ, Dept Automat, Xiamen 361005, Peoples R China

[2] Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2V4, Canada

来源：

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2015年 / 62卷 / 02期

基金：

高等学校博士学科点专项科研基金; 中国国家自然科学基金; 加拿大自然科学与工程研究理事会;

关键词：

Data-driven approach; expectation-maximization (EM) algorithm; fault diagnosis; missing data; CONTROL LOOP DIAGNOSIS; TOLERANT CONTROL; NEURAL-NETWORK; KALMAN FILTER; SYSTEMS; IDENTIFICATION; MODELS; SPACE; MPC;

D O I：

10.1109/TIE.2014.2336635

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper introduces a data-driven approach for fault diagnosis in the presence of incomplete monitor data. The expectation-maximization (EM) algorithm is applied to handle missing data in order to obtain a maximum-likelihood solution for the discrete (or categorical) distribution. Because of the nature of categorical distributions, the maximization step of the EM algorithm is shown in this paper to have an easily calculated analytical solution, making this method computationally simple. An experimental study on a ball-and-tube system is investigated to demonstrate advantages of the proposed approach.

引用

页码：1231 / 1240

页数：10

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