NDLI: Condition monitoring of brushless DC motors with non-stationary dynamic conditions

Content Provider	IEEE Xplore Digital Library
Author	Zubizarreta-Rodriguez, J.F. Vasudevan, S.
Copyright Year	2014
Description	Author affiliation: Australian Centre for Field Robot., Univ. of Sydney, Sydney, NSW, Australia (Zubizarreta-Rodriguez, J.F.; Vasudevan, S.)
Abstract	This work introduces a new multi-sensor measurement framework for condition monitoring of brushless DC motors (BLDCM) with bearings. An experimental platform for equipment health monitoring is used for producing different faults on BLDCMs and log the measurement data. This work is oriented to maximize the life-cycle of industrial machinery and prevent catastrophic failures and their environmental consequences through reliable behavior classification. A public benchmark data set containing key failure scenarios is being built based on this work. This data set will be unique with respect to other available data sets due to the different sensors used and include more extensive scenarios such as non-stationary (time varying) conditions. A BLDCM with a bearing is tested under non-stationary conditions, and the scenario for their failure is developed. Supervised learning classifiers such as back propagation neural network and support vector machine are used to identify the fault state in the equipment.
Starting Page	62
Ending Page	67
File Size	1904973
Page Count	6
File Format	PDF
ISBN	9781467363860
DOI	10.1109/I2MTC.2014.6860523
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-12
Publisher Place	Uruguay
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Testing Sensors Force Voltage measurement Support vector machines Monitoring Current measurement Brushless DC Motors Multi-sensor Measurements Condition Monitoring Signal Processing
Content Type	Text
Resource Type	Article

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