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Generically Dependent Continuant
Program and Abstract Book - 2010 7th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2010
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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Information Content Entity
Conference Paper
Data fusion for multiple mechanical fault diagnosis in induction motors at variable operating conditions
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.
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.
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