Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform
Unit of Research: Control, Monitoring and Reliability of the Systems, Higher School of Sciences and Technology of Tunis, 5, Taha Hussein Street, Tunis, Postal Box. 56, Bab Menara 1008, Tunisia. ISA Transactions
(Impact Factor: 2.98).
06/2012; 51(5):566-72. DOI: 10.1016/j.isatra.2012.06.002
In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor.
Available from: Lotfi Saidi
- "Then the clustering binary tree is used to classify the fault categories. In , an original fault signature based on an improved combination of Hilbert and Park transforms has been proposed to diagnose electrical and mechanical failures in IMs. SVM is used to classify electrical and mechanical faults in IMs based on Hilbret and Park features. "
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ABSTRACT: Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.
ISA Transactions 10/2014; 54. DOI:10.1016/j.isatra.2014.08.007 · 2.98 Impact Factor
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ABSTRACT: The fault diagnosis of multivariable dynamic system is researched based on nonlinear spectrum data and support vector machine. In order to overcome the problem of calculated amount expansion of generalized frequency response function description, the nonlinear spectrum feature is obtained based on one dimensional nonlinear output frequency response function. A frequency domain variable step size normalized LMS adaptive identification algorithm is proposed. The step size is changed instantaneously by using estimation error, so the convergence rate and steady state error are both considered. After obtain nonlinear spectrum feature, least square support vector machine is used to construct multi-fault classifier for fault identification. In order to reduce training time, support vector machine is trained by conjugate gradient algorithm based on simplified formula. The fault diagnosis of a vibration system with two inputs and four outputs is studied. The experiment results indicate that the proposed fault diagnosis method has short training time and high recognition rate so that it can meet the demand of online diagnosis.
Control and Decision Conference (CCDC), 2013 25th Chinese; 01/2013
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ABSTRACT: Although reconstructed phase space is one of the most powerful methods for analyzing a time series, it can fail in fault diagnosis of an induction motor when the appropriate pre-processing is not performed. Therefore, boundary analysis based a new feature extraction method in phase space is proposed for diagnosis of induction motor faults. The proposed approach requires the measurement of one phase current signal to construct the phase space representation. Each phase space is converted into an image, and the boundary of each image is extracted by a boundary detection algorithm. A fuzzy decision tree has been designed to detect broken rotor bars and broken connector faults. The results indicate that the proposed approach has a higher recognition rate than other methods on the same dataset.
ISA Transactions 11/2013; 53(2). DOI:10.1016/j.isatra.2013.11.004 · 2.98 Impact Factor
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