Diagnosis of switch open-circuit fault in PM brushless DC motor drives

被引：18

作者：

Awadallah, MA ^{[1
]}

Morcos, AA ^{[1
]}

机构：

[1] Kansas State Univ, Dept Elect & Comp Engn, Manhattan, KS 66502 USA

来源：

LESCOPE'03 : 2003 LARGE ENGINEERING SYSTEMS CONFERENCE ON POWER ENGINEERING, CONFERENCE PROCEEDINGS: ENERGY FOR THE FUTURE | 2003年

关键词：

machine fault diagnosis; wavelet transform; adaptive neuro-fuzzy systems;

D O I：

10.1109/LESCPE.2003.1204682

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 [动力工程及工程热物理]; 0820 [石油与天然气工程];

摘要：

An adaptive neuro-fuzzy inference system (ANFIS) is developed to diagnose open switch faults in the inverter bridge of PM brushless DC motor drives. Performance of the drive system under normal and faulty conditions is obtained through a discrete-time model. The motor DC-link current is monitored over one electrical cycle under healthy and faulty operations. Time-domain waveform is processed using wavelet transform, and suitable indices are derived to train ANFIS. Testing of the diagnosing system shows the effectiveness of the proposed methodology.

引用

页码：69 / 73

页数：5

共 8 条

[1]

Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis [J].

Altug, S ;

Chow, MY ;

Trussell, HJ .

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 1999, 46 (06) :1069-1079

[2]

On line diagnosis of defaults on squirrel cage motors using FEM [J].

Bentounsi, A ;

Nicolas, A .

IEEE TRANSACTIONS ON MAGNETICS, 1998, 34 (05) :3511-3514

[3]

DEBEBE K, 1992, P IEEE IAS ANN M, V1, P92

[4]

DYNAMIC MODELING OF BRUSHLESS DC MOTORS FOR AEROSPACE ACTUATION [J].

DEMERDASH, NA ;

NEHL, TW .

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 1980, 16 (06) :811-821

[5]

DYNAMIC SIMULATION OF RADIALLY ORIENTED PERMANENT MAGNET-TYPE ELECTRONICALLY OPERATED SYNCHRONOUS MACHINES WITH PARAMETERS OBTAINED FROM FINITE-ELEMENT FIELD SOLUTIONS [J].

NEHL, TW ;

FOUAD, FA ;

DEMERDASH, NA ;

MASLOWSKI, EA .

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 1982, 18 (02) :172-182

[6]

FEASIBILITY OF USING UNSUPERVISED LEARNING, ARTIFICIAL NEURAL NETWORKS FOR THE CONDITION MONITORING OF ELECTRICAL MACHINES [J].

PENMAN, J ;

YIN, CM .

IEE PROCEEDINGS-ELECTRIC POWER APPLICATIONS, 1994, 141 (06) :317-322

[7]

Motor bearing damage detection using stator current monitoring [J].

Schoen, RR ;

Habetler, TG ;

Kamran, F ;

Bartheld, RG .

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 1995, 31 (06) :1274-1279

[8]

An adaptive statistical time-frequency method for detection of broken bars and bearing faults in motors using stator current [J].

Yazici, B ;

Kliman, GB .

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 1999, 35 (02) :442-452

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