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Program and Abstract Book - 2010 7th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2010
type: Book
type: Entity
type: Proceedings
publication venue for: Data fusion for multiple mechanical fault diagnosis in induction motors at variable operating conditions
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type: Information Content Entity
type: Generically Dependent Continuant
type: Document
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RDF description of Data fusion for multiple mechanical fault diagnosis in induction motors at variable operating conditions - http://orbis.uaslp.mx/vivo/individual/scopus8779
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date: 2026-08-28T02:11:29
publisher: vivo
type: Document

Article
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Conference Paper
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Data fusion for multiple mechanical fault diagnosis in induction motors at variable operating conditions
type: Conference Paper
page end: 181
date time value: conference paper date 8780
type: Article
type: Generically Dependent Continuant
doi: 10.1109/ICEEE.2010.5608632
has publication venue: Program and Abstract Book - 2010 7th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2010
most specific type: Document
related by: authorship_62671
type: Entity
origen: scopus
type: Continuant
artnum: 5608632
page start: 176
crisid: 8780
abstract: In this paper, data fusion based on multi-class support vector machines (SVM) is presented to detect and isolate three mechanical faults in induction motors. First, we construct the feature vector by using signatures created from frequency-domain characteristics. These signatures are obtained from mechanical vibration and line currents measurements. Then, the feature vector is used to feed SVM%27s to classify different motor conditions (normal, misalignment, unbalanced and bearing fault). Different experiments using a three phase induction motor were performed under variable operational conditions (motor speeds and load torque scenarios) in order to acquire training and validation data. The identified optimal parameters of the SVM%27s are reported. The SVM%27s are studied with two types of kernel functions, the radial basis and the polynomial functions. Data acquisition, feature extraction and SVM%27s computation were implemented by using LabView programming language. The experimental results show the effectiveness of the proposed approach in diagnosing the studied mechanical faults at different speeds and load conditions. In these experimental tests, the worst-case accuracy of the proposed method was 97.1%25. © 2010 IEEE.
type: Thing
related by: authorship_62670
type: Information Content Entity
abstract: In this paper, data fusion based on multi-class support vector machines (SVM) is presented to detect and isolate three mechanical faults in induction motors. First, we construct the feature vector by using signatures created from frequency-domain characteristics. These signatures are obtained from mechanical vibration and line currents measurements. Then, the feature vector is used to feed SVM's to classify different motor conditions (normal, misalignment, unbalanced and bearing fault). Different experiments using a three phase induction motor were performed under variable operational conditions (motor speeds and load torque scenarios) in order to acquire training and validation data. The identified optimal parameters of the SVM's are reported. The SVM's are studied with two types of kernel functions, the radial basis and the polynomial functions. Data acquisition, feature extraction and SVM's computation were implemented by using LabView programming language. The experimental results show the effectiveness of the proposed approach in diagnosing the studied mechanical faults at different speeds and load conditions. In these experimental tests, the worst-case accuracy of the proposed method was 97.1%25. © 2010 IEEE.
type: Document
freetext keyword: Data fusion; Fault diagnosis; Induction motors; Support vector machines Bearing fault; Different speed; Experimental test; Fault diagnosis; Feature vectors; Frequency domains; Kernel function; LabVIEW programming; Line currents; Load condition; Load torques; Mechanical fault diagnosis; Mechanical faults; Mechanical vibrations; Motor speed; Multi-class support vector machines; Operational conditions; Optimal parameter; Polynomial functions; Radial basis; Support vector; Three phase induction motor; Validation data; Variable operating condition; Automation; Control; Data fusion; Electrical engineering; Feature extraction; Information fusion; Process control; Quality assurance; Reliability; Support vector machines; Vectors; Vibrations (mechanical); Induction motors
most specific type: Conference Paper
estado: published
EI dscopus: 2-s2.0-78650281303
related by: authorship_62672