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Research Papers

A General Approach for Current-Based Condition Monitoring of Induction Motors

[+] Author and Article Information
W. J. Bradley

Powell Switchgear & Instrumentation Ltd.,
Bradford BD4 7EH, UK

M. K. Ebrahimi

School of Engineering,
University of Bradford,
Bradford BD7 1DP, UK
e-mail: m.ebrahimi@bradford.ac.uk

M. Ehsani

Department of Electrical and
Computer Engineering,
Texas A&M University,
College Station, TX 77843

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received April 26, 2013; final manuscript received February 9, 2014; published online April 28, 2014. Assoc. Editor: Ryozo Nagamune.

J. Dyn. Sys., Meas., Control 136(4), 041024 (Apr 28, 2014) (12 pages) Paper No: DS-13-1171; doi: 10.1115/1.4026874 History: Received April 26, 2013; Revised February 09, 2014

The development and validation of a novel current-based induction motor (IM) condition monitoring (CM) system is described. The system utilizes only current and voltage signals and conducts fault detection using a combination of model-based and model-free (motor current signature analysis) fault detection methods. The residuals (or fault indicator values) generated by these methods are analyzed by a fuzzy logic diagnosis algorithm that provides a diagnosis with regard to the health of the induction motor. Specifically, this includes an indication of the health of the major induction motor subsystems, namely the stator windings, the rotor cage, the rolling element bearings, and the air-gap (eccentricity). The paper presents the overall system concept, the induction motor models, development of parameter estimation techniques, fault detection methods, and the fuzzy logic diagnosis algorithm and includes results from 110 different test cases involving four 7.5 kW four pole squirrel cage motors. The results show good performance for the four chosen faults and demonstrate the potential of the system to be used as an industrial condition monitoring tool.

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Figures

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Fig. 3

Detailed flow diagram of the entire fault detection algorithm (condition monitoring stage)

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Fig. 2

An overview of the condition monitoring stage

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Fig. 1

An overview of the baseline creation stage

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Fig. 7

Bearing fault indicator residual values (positive values indicate successful fault detection, negative values indicate no fault detected)

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Fig. 8

Rotor fault membership function

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Fig. 9

The output membership functions map the fuzzy outputs to precise numerical values

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Fig. 10

The process of fuzzy logic diagnosis for 0% fault on the stator

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Fig. 11

Fault detection, diagnosis, and isolation results for bearing, air-gap, stator, and rotor faults

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Fig. 4

Estimated rotor fault parameter plotted against section of rotor bar removed

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Fig. 6

Air-gap fault indicator residual values (positive values indicate successful fault detection, negative values indicate no fault detected)

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Fig. 5

Estimated stator fault parameter versus the number of shorted turns on winding A

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