مقالات رزومه دکتر سعید نظامیوند چگینی

Intelligent fault detection diagnosis methods are one of the common topics in recently investigations. In this paper, a new hybrid technique is presented based on discrete wavelet transform (DWT) and multi – class support vector machine (Multi-SVM). The considered vibrational signals are collected in three conditions: normal, chipped tooth and worn teeth. These signals are decomposed using DWT methods with different wavelet base functions and the most appropriate level of decomposition are selected by the cross - correlation concept. The feature vector for each sample is extracted using different time domain statistical functions. «One – against - one» support vector machine (SVM-OAO) is utilized for detecting the gearbox conditions. The condition recognition of a gearbox is depended on the extracted features type and setting the SVM parameters. Therefore, in this study, particle swarm optimization (PSO) is used for identifying the most sensitive features to the defect and its type and determining the optimal parameters of SVM method. The obtained results show that the identification accuracy of the gearbox conditions is significantly increased with improving the feature matrix and the SVM classifier method.

In this paper, a new method for removing the noise from the vibration signals acquired from the rotating machinery for its condition monitoring is presented. Firstly, each signal is decomposed into its modes using the empirical mode decomposition method. Then for distinguishing the noisy modes from the noise-free modes, the similarity measure between the probability density function of the raw signal and its modes is calculated. Finally, the noise-dominate modes are denoised by the improved thresholding function, and the denoised signal is reconstructed. In this study, the proposed method is implemented for denoising the simulated signal and real data corresponding to different bearing conditions. Finally, the kurtosis and the envelope spectrum of the denoised signal are calculated for detecting the fault presence and its type. The results show that the proposed technique can improve the quality of the reconstructed signals so that the sensitivity of the kurtosis factor to the presence of the defect in the inner and outer rings is increased. Also, the defects frequencies appear in the spectrum of the signals denoised, and the fault type can easily be detected. The results indicate that the proposed denoising technique is superior to the conventional empirical mode decomposition-based denoising method.

In this paper, a new hybrid intelligent method is suggested for the bearing fault detection at time – varying speed conditions. The vibration signals have been collected for two states as healthy bearing and defected inner race under variable speeds. In this study, the ensemble empirical mode decomposition (EEMD) technique and Johanson trace method are utilized for extracting the co-integration relationships from the vibration data. Then, the feature matrix corresponding to the co-integration relationships is calculated using the wavelet packet decomposition (WPD) method, and the time -domain statistical features. In the next stage,the compensation distance evaluation technique (CDET) has been used to determine the preselected feature subsets. The preselected features are utilized as input data of the support vector machine (SVM) to predict the bearing state. Finally, The optimal SVM parameters and the optimal feature subsets are determined using the binary particle swarm optimization (BPSO) algorithm. The obtained results demonstrate that the optimal features are well able to differentiate between different bearing states at time-varying speeds. Comparing the results of this article with other fault detection methods indicates the ability of the proposed method.

In this paper, a new method is presented for removing the noise from the vibration signals of the rotating machinery based on the empirical wavelet transform (EWT) and the soft thresholding function. The EWT is a new signal processing method that decomposes each signal into its constituent components based on its frequency information. After decomposing each signal, the soft thresholding method is performed to empirical modes and the denoised signal is reconstructed. For evaluating the proposed denoising approach, this technique is used for detecting the bearing fault. For this purpose, the kurtosis factor and the envelope spectrum of each denoised signal are calculated for detecting the presence of fault and diagnosing the fault type, respectively. The results illustrate that the proposed technique increases the quality of the vibration signals so that the obtained kurtosis value is more sensitive to the presence of fault in the inner ring and the outer ring. On the other hand, the type of fault is diagnosed by observing the appeared frequencies in the envelope spectrum of signals denoised with EWT. The results show that the EWT-based denoising approach is superior to the empirical mode decomposition-based denoising method in the rotating machinery fault diagnosis procedure.

In this paper, a new method is presented for gear fault detection. The vibrational signals of gearbox set are collected in three conditions: normal, chipped and worn teeth. These signals are adaptively decomposed into a number of intrinsic mode functions (IMFs) by the empirical mode decomposition (EMD). Since, all of the IMFs drived from the EMD are not appropriate for fault detection, the cross-corrolation concept is used to select all most apptoptiate IMFs. Then, feature matrix corresponding to each condition, is extracted using statistical functions. “One-against-one” support vector machine (SVM-OAO) is utilized to classify the faults. Since, all of the extracted features are not suitable for fault detection and SVM has parameters to be set, the particle swarm optimization (PSO) is used to select the best feature and detect optimal parameters of SVM. Objective function in this paper is accuracy of the SVM classifier in predicting of gearbox condition. Obtained results show that the selected features in this method and optimized SVM have the excellent ability to classify the faults.