A Two-Stage Fault Detection and Isolation Platform for Industrial Systems Using Residual Evaluation

被引：49

作者：

Heydarzadeh, Mehrdad ^{[1
]}

Nourani, Mehrdad ^{[1
]}

机构：

[1] Univ Texas Dallas, Dept Elect Engn, Richardson, TX 75080 USA

来源：

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | 2016年 / 65卷 / 10期

关键词：

Discrete wavelet transforms (DWTs); fault detection; fault diagnosis; pneumatic actuators; residual analysis; support vector machines; system identification; DIAGNOSIS;

D O I：

10.1109/TIM.2016.2575179

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Fault detection and isolation (FDI) is an important part of modern industrial systems, and plays a vital role in maintainability, safety, and reliability of process. We propose a novel FDI architecture based on a predictive model for fault-free process. We use the least-square support vector machine for identifying a nonlinear system and detecting its faults that may occur. Wavelet analysis on residual is used for fault isolation. An average accuracy of 95% has been achieved for all the abrupt faults of the well-known development and application of methods for actuator diagnosis in industrial control systems benchmark.

引用

页码：2424 / 2432

页数：9

共 27 条

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