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
[1]   Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV [J].
Abbaspour, Alireza ;
Aboutalebi, Payam ;
Yen, Kang K. ;
Sargolzaei, Arman .
ISA TRANSACTIONS, 2017, 67 :317-329
[2]   Detection and classification of bearing faults in industrial geared motors using temporal features and adaptive neuro-fuzzy inference system [J].
Abdelkrim, Choug ;
Meridjet, Mohamed Salah ;
Boutasseta, Nadir ;
Boulanouar, Lakhdar .
HELIYON, 2019, 5 (08)
[3]  
Abouelanouar B., 2018, INT J ENG TECHNOLOGY, V7
[4]   A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling [J].
Al-Bugharbee, Hussein ;
Trendafilova, Irina .
JOURNAL OF SOUND AND VIBRATION, 2016, 369 :246-265
[5]   A Bibliometric Review and Analysis of Data-Driven Fault Detection and Diagnosis Methods for Process Systems [J].
Alauddin, Md ;
Khan, Faisal ;
Imtiaz, Syed ;
Ahmed, Salim .
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2018, 57 (32) :10719-10735
[6]   Actuator fault detection and isolation on a quadrotor unmanned aerial vehicle modeled as a linear parameter-varying system [J].
Alberto Guzman-Rabasa, Julio ;
Ronay Lopez-Estrada, Francisco ;
Manuel Gonzalez-Contreras, Brian ;
Valencia-Palomo, Guillermo ;
Chadli, Mohammed ;
Perez-Patricio, Madain .
MEASUREMENT & CONTROL, 2019, 52 (9-10) :1228-1239
[7]   Denoising of Heart Sound Signals Using Discrete Wavelet Transform [J].
Ali, Mohammed Nabih ;
El-Dahshan, EL-Sayed A. ;
Yahia, Ashraf H. .
CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2017, 36 (11) :4482-4497
[8]  
Aminu K.T., 2020, INT J ADV SCI RES EN, V6, P180, DOI [10.31695/IJASRE.2020.33692, DOI 10.31695/IJASRE.2020.33692]
[9]  
Bougatef Z, 2018, INT MULTICONF SYST, P146, DOI 10.1109/SSD.2018.8570436
[10]   Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery [J].
Brkovic, Aleksandar ;
Gajic, Dragoljub ;
Gligorijevic, Jovan ;
Savic-Gajic, Ivana ;
Georgieva, Olga ;
Di Gennaro, Stefano .
ENERGY, 2017, 136 :63-71