Fault diagnosis of monoblock centrifugal pump using SVM
نویسندگان
چکیده
منابع مشابه
Svm Based Fault Diagnosis of Monoblock Centrifugal Pump Using Stationary Wavelet Features
Fault diagnosis of monoblock centrifugal pump essentially forms a pattern recognition problem. There are three important steps to be performed in pattern recognition namely feature extraction, feature selection and classification. In this study, stationary wavelet transform (SWT) is used for feature extraction and SMO algorithm (a WEKA implementation of Support Vector Machine (SVM) algorithm) i...
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Fault diagnosis of monoblock centrifugal pump is conceived as a pattern recognition problem. There are three important steps to be performed in pattern recognition namely feature extraction, feature selection and classification. In this study, Stationary wavelet transform (SWT) is used for feature extraction from the input signals and Bayes net classifier is used for classification. A WEKA impl...
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Fault diagnosis of monoblock centrifugal pump is conceived as a pattern recognition problem. There are three important phases involved in a pattern recognition namely feature extraction, feature selection and classification. In this study, stationary wavelet transform (SWT) is used for feature extraction and J48 algorithm is used for feature selection and classification. The different fault con...
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Abstract: Fault detection and diagnosis has an effective role for the safe operation and long life of systems. Condition monitoring is an appropriate way of the maintenance techniques which is applicable in the fault diagnosis of rotating machinery faults. We considered the Support Vector Machine (SVM) method for classifying the condition of centrifugal pump into two types of faults through six...
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This paper presents a fault diagnosis method for a centrifugal pump system with frequency-domain symptom parameters by using the wavelet transform, rough sets and a fuzzy neural network to detect faults and distinguish fault types at an early stage. The wavelet transform is used for feature extraction across an optimum frequency region. The diagnosis knowledge for the training of neural network...
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ژورنال
عنوان ژورنال: Engineering Science and Technology, an International Journal
سال: 2014
ISSN: 2215-0986
DOI: 10.1016/j.jestch.2014.04.005