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To achieve planetary gearbox fault classification, vibration signal analysis has been widely employed with rich information about the health status and easy measurement. It is critical to extract features with enough health status information for fault classification. The self-adaptation of ensemble empirical mode decomposition (EEMD) indicates the dependence between the raw vibration signal and EEMD-decomposed intrinsic mode functions (IMFs). In this study, we develop a novel fault feature vector based on the dependence. To develop the dependence-based feature vector, simulated vibration signals with different sun gear tooth crack levels are analyzed. The dependence between the raw signal and each IMF is investigated by Archimedean copulas. With the goodness-of-fit test, the copula estimation closest to the perfect fit is selected for dependence representation. The parameter of the selected copula is applied to develop the dependence-based feature vector. To test the ability of the dependence-based feature vector in fault classification for a real planetary gearbox, experimental vibration signals with different gear fault levels at different gears are classified by a multi-class support vector machine. The classification accuracy of the developed feature vector is compared with that of a reported indicator. Results show the dependence-based feature vector provides higher classification accuracy than the reported, indicating the developed feature vector contains more health status information. The developed feature vector can serve better for planetary gearbox fault classification. [ABSTRACT FROM AUTHOR]

Fault diagnosis of rotating machinery is receiving more and more attentions. Vibration signals of rotating machinery are commonly analyzed to extract features of faults, and the features are identified with classifiers, e.g. artificial neural networks (ANNs) and support vector machines (SVMs). Due to nonlinear behaviors and unknown noises in machinery, the extracted features are varying from sample to sample, which may result in false classifications. It is also difficult to analytically ensure the accuracy of fault diagnosis. In this paper, a feature extraction and evaluation method is proposed for fault diagnosis of rotating machinery. Based on the central limit theory, an extraction procedure is given to obtain the statistical features with the help of existing signal processing tools. The obtained statistical features approximately obey normal distributions. They can significantly improve the performance of fault classification, and it is verified by taking ANN and SVM classifiers as examples. Then the statistical features are evaluated with a decoupling technique and compared with thresholds to make the decision on fault classification. The proposed evaluation method only requires simple algebraic computation, and the accuracy of fault classification can be analytically guaranteed in terms of the so-called false classification rate (FCR). An experiment is carried out to verify the effectiveness of the proposed method, where the unbalanced fault of rotor, inner race fault, outer race fault and ball fault of bearings are considered. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes singular value decomposition (SVD)-based feature extraction methods for fault classification of an induction motor: a short-time energy (STE) plus SVD technique in the time-domain analysis, and a discrete cosine transform (DCT) plus SVD technique in the frequency-domain analysis. To early identify induction motor faults, the extracted features are utilized as the inputs of multi-layer support vector machines (MLSVMs). Since SVMs perform well with the radial basis function (RBF) kernel for appropriately categorizing the faults of the induction motor, it is important to explore the impact of the values for the RBF kernel, which affects the classification accuracy. Likewise, this paper quantitatively evaluates the classification accuracy with different numbers of features, because the number of features affects the classification accuracy. According to the experimental results, although SVD-based features are effective for a noiseless environment, the STE plus SVD feature extraction approach is more effective with and without sensor noise in terms of the classification accuracy than the DCT plus SVD feature extraction approach. To demonstrate the improved classification of the proposed approach for identifying faults of the induction motor, the proposed SVD based feature extraction approach is compared with other state-of-the art methods and yields higher classification accuracies for both noiseless and noisy environments than conventional approaches. [Copyright &y& Elsevier]