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
[41]  
Luo GY, 2019, PROCEEDINGS OF THE 2019 IEEE 12TH INTERNATIONAL SYMPOSIUM ON DIAGNOSTICS FOR ELECTRICAL MACHINES, POWER ELECTRONICS AND DRIVES (SDEMPED), P338, DOI [10.1109/demped.2019.8864829, 10.1109/DEMPED.2019.8864829]
[42]   Review of two non-centralized observer-based diagnosis schemes for interconnected systems [J].
Martinez-Villegas, Cesar T. ;
Theilliol, Didier ;
Torres, Lizeth .
IFAC PAPERSONLINE, 2018, 51 (24) :550-557
[43]  
Min Lee Kyeong, 2017, [JOURNAL OF KOREA MULTIMEDIA SOCIETY, 멀티미디어학회논문지], V20, P1299, DOI 10.9717/kmms.2017.20.8.1299
[44]  
Mishra KM, 2019, IEEE INT C EMERG, P904, DOI [10.1109/etfa.2019.8869230, 10.1109/ETFA.2019.8869230]
[45]  
Mnassri B., 2009, P 7 IFAC S FAULT DET, P834
[46]   Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems [J].
Mohd Amiruddin, Ahmad Azharuddin Azhari ;
Zabiri, Haslinda ;
Taqvi, Syed Ali Ammar ;
Tufa, Lemma Dendena .
NEURAL COMPUTING & APPLICATIONS, 2020, 32 (02) :447-472
[47]   Fault-Tolerant Control of Quadcopter UAVs Using Robust Adaptive Sliding Mode Approach [J].
Ngoc Phi Nguyen ;
Hong, Sung Kyung .
ENERGIES, 2019, 12 (01)
[48]   A review of data-driven fault detection and diagnosis methods: applications in chemical process systems [J].
Nor, Norazwan Md ;
Hassan, Che Rosmani Che ;
Hussain, Mohd Azlan .
REVIEWS IN CHEMICAL ENGINEERING, 2020, 36 (04) :513-553
[49]   Detection and classification of faults in a microgrid using wavelet neural network [J].
Panigrahi, Basanta K. ;
Ray, Prakash K. ;
Rout, Pravat K. ;
Mohanty, Asit ;
Pal, Kumaresh .
JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES, 2018, 39 (01)
[50]   Wind turbine fault detection based on expanded linguistic terms and rules using non-singleton fuzzy logic [J].
Qu, Fuming ;
Liu, Jinhai ;
Zhu, Hongfei ;
Zhou, Bowen .
APPLIED ENERGY, 2020, 262