In order to maintain continuous production and to avoid the maintenance cost increment in power plants, it is important to monitor the condition of equipment, especially the generator. Regarding the impossibility of direct access to rotating diodes in brushless synchronous generators, the condition monitoring of these elements is very important. In this paper, a novel fault detection method is proposed for the diode rectifier of brushless synchronous generator. At the first stage of this method, the vibration signals are recorded and feature extraction is performed by calculating the relative energy of discrete wavelet transform components. Multiclass support vector machine (MSVM) is used for classification, and the best mother wavelet and number of decomposition level are chosen based on classification performance. To enhance the performance of the classification, a modified sequential forward subset selection approach is included by which the best statistical features are selected. In this approach, besides selecting the best subset of statistical features, the classification parameter is tuned according to the selected subset to achieve the best performance. The result of the proposed method is eventually compared with those results of classification performance using conventional subset selection. Experimental results show that the proposed method can detect rectifier faults effectively.
Fast and accurate fault detection is a vital issue for all engineering systems, especially for those which generate power for sensitive applications. To achieve rapid and accurate fault detection, an appropriate signal must be measured and the best features of that signal should be extracted. In this paper, an approach is proposed to detect the healthy and faulty states of a rotating diode rectifier in a brushless synchronous generator by using three-phase terminal voltage. There is not any direct access to the rectifier, and therefore, fault detection must be performed using signals from the machine itself. In order to extract the correct voltage signal, fast Fourier analysis is performed on the signal, and to select the best frequency to reduce redundancy and increase classification accuracy, a wrapper-based feature selection approach is utilised. In this approach, the best frequency is selected from among the frequencies generated according to the accuracy of the classifier, a Support Vector Machine (SVM). The other frequencies are then added one by one according to their accuracy in each step, and the best subset, that is, the one with the best accuracy for the classifier, is selected. The proposed approach is then evaluated using experimental data.
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