Fault Detection and Diagnostics for Non-Intrusive Monitoring using Motor Harmonics

被引：12

作者：

Orji, Uzoma A. ^{[1
]}

Remscrim, Zachary ^{[1
]}

Laughman, Christopher ^{[1
]}

Leeb, Steven B. ^{[1
]}

Wichakool, Warit ^{[1
]}

Schantz, Christopher ^{[1
]}

Cox, Robert ^{[1
]}

Paris, James ^{[1
]}

Kirtley, James L., Jr. ^{[1
]}

Norford, Les K. ^{[1
]}

机构：

[1] MIT, Cambridge, MA 02139 USA

来源：

2010 TWENTY-FIFTH ANNUAL IEEE APPLIED POWER ELECTRONICS CONFERENCE AND EXPOSITION (APEC) | 2010年

关键词：

AIRGAP ECCENTRICITY; INDUCTION-MOTOR; ROTOR; SPECTRUM; BARS;

D O I：

10.1109/APEC.2010.5433437

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：

Harmonic analysis of motor current has been used to track the speed of motors for sensorless control. Algorithms exist that track the speed of a motor given a dedicated stator current measurement, for example [1-5]. Harmonic analysis has also been applied for diagnostic detection of electro-mechanical faults such as damaged bearings and rotor eccentricity [6-17]. This paper demonstrates the utility of harmonic analysis for fault detection and diagnostics in non-intrusive monitoring applications, where multiple loads are tracked by a sensor monitoring only the aggregate utility service. An optimization routine is implemented to maintain accuracy of speed estimation while using shorter lengths of data.

引用

页码：1547 / 1554

页数：8

共 50 条

[41] Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs) [J].

Konar, P. ;

Chattopadhyay, P. .

APPLIED SOFT COMPUTING, 2011, 11 (06) :4203-4211

[42] Induction Motor Fault Detection and Diagnosis using KDE and Kullback-Leibler Divergence [J].

Ferracuti, Francesco ;

Giantomassi, Andrea ;

Iarlori, Sabrina ;

Ippoliti, Gianluca ;

Longhi, Sauro .

39TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY (IECON 2013), 2013, :2923-2928

[43] Bearing fault detection using wavelet packet transform of induction motor stator current [J].

Zarei, Jafar ;

Poshtan, Javad .

TRIBOLOGY INTERNATIONAL, 2007, 40 (05) :763-769

[44] Detection of broken rotor bar fault in an induction motor using convolution neural network [J].

Gundewar, Swapnil ;

Kane, Prasad ;

Andhare, Atul .

JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING, 2022, 16 (02)

[45] ARM Based Induction Motor Fault Detection Using Wavelet and Support Vector Machine [J].

Jagadanand, G. ;

Dias, Fedora Lia .

2015 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, INFORMATICS, COMMUNICATION AND ENERGY SYSTEMS (SPICES), 2015,

[46] Optimal feature selection using genetic algorithm for mechanical fault detection of induction motor [J].

Ngoc-Tu Nguyen ;

Hong-Hee Lee ;

Jeong-Min Kwon .

Journal of Mechanical Science and Technology, 2008, 22 :490-496

[47] Bearing Fault Detection of Induction Motor Using SWPT and DAG Support Vector Machines [J].

Ben Abid, Firas ;

Zgarni, Slaheddine ;

Braham, Ahmed .

PROCEEDINGS OF THE IECON 2016 - 42ND ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2016, :1476-1481

[48] Mechanical fault diagnostics for induction motor with variable speed drives using Adaptive Neuro-fuzzy Inference System [J].

Ye, Z ;

Sadeghian, A ;

Wu, B .

ELECTRIC POWER SYSTEMS RESEARCH, 2006, 76 (9-10) :742-752

[49] Fault Detection and Diagnosis of Multi-Phase Induction Motor Drives Using MFRF Technique [J].

Annamalai, Balamurugan ;

Swaminathan, Sivakumaran Thangavel .

2020 5TH INTERNATIONAL CONFERENCE ON DEVICES, CIRCUITS AND SYSTEMS (ICDCS' 20), 2020, :247-251

[50] Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning [J].

Manjeevan Seera ;

Chee Peng Lim ;

Chu Kiong Loo .

Journal of Intelligent Manufacturing, 2016, 27 :1273-1285

← 1 2 3 4 5 →