Active fault diagnosis for uncertain systems using optimal test designs and detection through classification

被引:23
作者
Palmer, Kyle A. [1 ]
Bollas, George M. [1 ]
机构
[1] Univ Connecticut, Dept Chem & Biomol Engn, 191 Auditorium Rd,Unit 3222, Storrs, CT 06269 USA
关键词
Active fault diagnosis; Optimal test design; Fault detection; Principal component analysis; k-NN; ARTIFICIAL NEURAL-NETWORK; K-NEAREST-NEIGHBOR; FAILURE-DETECTION; INPUT-DESIGN; SENSITIVITY;
D O I
10.1016/j.isatra.2019.02.034
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Fault detection and isolation (FDI) is becoming increasingly difficult due to the complexity and uncertainty of modern systems. For industrial systems with explicit models available, model-based active FDI tests can improve the capability for fault diagnosis. These tests should be determined and evaluated prior to implementation to minimize on-site computational costs. In this paper, a methodology is presented for the design optimization and assessment of tests for active fault diagnosis. The objective is to maximize the information from system outputs with respect to faults while minimizing the correlation between faults and uncertainty. After a test is designed, it is deployed with a k-nearest neighbor algorithm combined with principal component analysis.Two case studies are used to verify the proposed methodology, a three-tank system and a diesel engine. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:354 / 369
页数:16
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