جستجوی مقالات مرتبط با کلیدواژه « gearbox » در نشریات گروه « مکانیک »

Identifying fault categories, especially for compound faults, is a challenging task in mechanical fault diagnosis. For this task, this paper proposes a novel intelligent method based on wavelet packet transform (WPT) and multiple classifier fusion. An unexpected damage on the gearbox may break the whole transmission line down. It is therefore crucial for engineers and researchers to monitor the health condition of the gearbox in a timely manner to eliminate the impending faults. However, useful fault detection information is often submerged in heavy background noise. The non-stationary vibration signals were analyzed to reveal the operation state of the gearbox. The proposed method is applied to the fault diagnosis of gears and bearings in the gearbox. The diagnosis results show that the proposed method is able to reliably identify the different fault categories which include both single fault and compound faults, which has a better classification performance compared to any one of the individual classifiers. The vibration dataset is used from a test rig in Shahrekord University and a gearbox from Sepahan Cement. Eventually, the gearbox faults are classified using these statistical features as input to WSVM.

This paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (LSSVM). Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared to select an appropriate wavelet for feature extraction. The fault diagnosis method consists of three steps, firstly the six different base wavelets are considered. Out of these six wavelets, the base wavelet is selected based on wavelet selection criterion to extract statistical features from wavelet coefficients of raw vibration signals. Based on wavelet selection criterion, Daubechies wavelet and Meyer are selected as the best base wavelet among the other wavelets considered from the Maximum Relative Energy and Maximum Energy to Shannon Entropy criteria respectively. Finally, the gearbox faults are classified using these statistical features as input to LSSVM technique. The optimal decomposition level of wavelet is selected based on the Maximum Energy to Shannon Entropy ratio criteria. In addition to this, Energy and Shannon Entropy of the wavelet coefficients are used as two new features along with other statistical parameters as input of the classifier. Some kernel functions and multi kernel function as a new method are used with three strategies for multi classification of gearboxes. The results of fault classification demonstrate that the LSSVM identified the fault categories of gearbox more accurately with multi kernel and OAOT strategy.

A new method based on principal component analysis (PCA) and artificial neural networks (ANN) is proposed for fault diagnosis of gearboxes. Firstly the six different base wavelets are considered, in which three are from real valued and other three from complex valued. Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared to select an appropriate wavelet for feature extraction next, the continuous wavelet coefficients (CWC) are evaluated for some different scales. As a new method, the optimal range of wavelet scales is selected based on the maximum energy to Shannon entropy ratio criteria and consequently feature vectors are reduced. In addition, energy and Shannon entropy of the wavelet coefficients are used as two new features along with other statistical parameters as input of the classifier. To prevent the curse of dimensionality problem, the principal component analysis applies to this set of features. Finally, the gearbox faults are classified using these statistical features as input to machine learning techniques. Four artificial neural networks are used for faults classifications. The test result showed that the MLP identified the fault categories of gearbox more accurately for both real wavelet and complex wavelet and has a better diagnosis performance as compared to the RBF, LVQ and SOM.