A Survey on Fault Detection and Diagnosis Methods

被引:18
作者
Avila Okada, Kenji Fabiano [1 ]
de Morais, Aniel Silva [1 ]
Oliveira-Lopes, Luis Claudio [2 ]
Ribeiro, Laura [1 ]
机构
[1] Univ Fed Uberlandia, Sch Elect Engn, Uberlandia, MG, Brazil
[2] Univ Fed Uberlandia, Sch Chem Engn, Uberlandia, MG, Brazil
来源
2021 14TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRY APPLICATIONS (INDUSCON) | 2021年
关键词
fault detection; fault diagnosis; signal analysis-based methods; model-based methods; data-driven methods; hybrid methods; DATA-DRIVEN; MODEL; ACTUATOR; SENSOR; SIGNAL; IDENTIFICATION; PROGNOSTICS; DESIGN; MOTOR;
D O I
10.1109/INDUSCON51756.2021.9529495
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Fault detection and diagnosis in modern control systems have been of constant interest in recent publications. Its progress is a consequence of the requirements imposed by the development of other technologies through demands on security operations, guarantee of the required functions execution, reduction of costs, and optimization of maintenance tasks. In order to provide the survey in this area, the article discriminates the main fault detection and diagnosis techniques, allowing the reader to acquire, in different practice scenarios, an ability to discern the possibilities of applying the methods in focus. The text is divided in signal analysis-based methods, model-based methods, data-driven methods, and hybrids methods. The conclusion exposes the main global limitations in the area as possible subjects for future works.
引用
收藏
页码:1422 / 1429
页数:8
相关论文
共 78 条
[61]   Fault Detection for Non-Gaussian Processes Using Multiple Canonical Correlation Analysis Models and Box-Cox Transformation [J].
Wang, Ping ;
Long, Zhiqiang ;
Lv, Zhiguo ;
Wang, Zhiqiang .
IEEE ACCESS, 2019, 7 :68707-68717
[62]   Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications [J].
Wang, Yanxue ;
Xiang, Jiawei ;
Markert, Richard ;
Liang, Ming .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2016, 66-67 :679-698
[63]  
Wang YL, 2019, CHIN AUTOM CONGR, P431, DOI [10.1109/CAC48633.2019.8996359, 10.1109/cac48633.2019.8996359]
[64]   Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information [J].
Wang, Zhanwei ;
Wang, Zhiwei ;
He, Suowei ;
Gu, Xiaowei ;
Yan, Zeng Feng .
APPLIED ENERGY, 2017, 188 :200-214
[65]   A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method [J].
Wen, Long ;
Li, Xinyu ;
Gao, Liang ;
Zhang, Yuyan .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2018, 65 (07) :5990-5998
[66]   Fault estimation of wind turbines using combined adaptive and parameter estimation schemes [J].
Witczak, Marcin ;
Rotondo, Damiano ;
Puig, Vicenc ;
Nejjari, Fatiha ;
Pazera, Marcin .
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2018, 32 (04) :549-567
[67]   Gaussian Mixture Model Using Semisupervised Learning for Probabilistic Fault Diagnosis Under New Data Categories [J].
Yan, Heng-Chao ;
Zhou, Jun-Hong ;
Pang, Chee Khiang .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2017, 66 (04) :723-733
[68]   Fault Diagnosis of Analog Filter Circuit Based on Genetic Algorithm [J].
Yang, Chenglin ;
Zhen, Liu ;
Hu, Cong .
IEEE ACCESS, 2019, 7 :54969-54980
[69]   Actuator and sensor faults estimation based on proportional integral observer for TS fuzzy model [J].
Youssef, T. ;
Chadli, M. ;
Karimi, H. R. ;
Wang, R. .
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2017, 354 (06) :2524-2542
[70]   A Bayesian wavelet packet denoising criterion for mechanical signal with non-Gaussian characteristic [J].
Yue, Guo-dong ;
Cui, Xiu-shi ;
Zou, Yuan-yuan ;
Bai, Xiao-tian ;
Wu, Yu-Hou ;
Shi, Huai-tao .
MEASUREMENT, 2019, 138 :702-712