Effective and efficient incipient fault diagnosis is vital to the maintenance and safe application of large-scale key mechanical system. variable predictive model-based class discrimination is a recently developed mul...
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Effective and efficient incipient fault diagnosis is vital to the maintenance and safe application of large-scale key mechanical system. variable predictive model-based class discrimination is a recently developed multiclassdiscrimination method and has been proved to be potential tool for multi-fault detection. However, the vibration signals from dynamic mechanical system always present non-normal distribution so that the original variable predictive model-based class discrimination might produce the inaccurate outcomes. An improved variable predictive model-based class discrimination method is introduced at first in this work. At the same time, variable predictive model-based class discrimination will suffer computation difficulty in the case of high-dimension input features. Therefore, a novel feature selection method based on similarity-fuzzy entropy is presented to boost the performance of the variable predictive model-based class discriminationclassifier. In this method, the ideal feature vectors are optimized to acquire more accurate similarity-fuzzy entropies for the input features. And, the one with the largest similarity-fuzzy entropy value is removed to refine input feature subset. Moreover, the optimal input features are repeatedly evaluated using the improved variable predictive model-based class discriminationclassifier until the expected results are achieved. Finally, the incipient multi-fault diagnosis model for a hydraulic piston pump is established and verified by experimental test. Some comparisons with commonly used methods were made, and the results indicate that the proposed method is more effective and efficient.
The pattern recognition of partial discharge (PD) is critical to evaluate the insulation condition and locate the defect of a power transformer. The existing pattern recognition methods fail to make use of the inter-r...
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The pattern recognition of partial discharge (PD) is critical to evaluate the insulation condition and locate the defect of a power transformer. The existing pattern recognition methods fail to make use of the inter-relations of the extracted features of PD signals. In fact, the inter-relations can show distinct dissimilarities between different classes of the signals. To overcome the defect of existing pattern recognition methods, the variable predictive model-based class discrimination (VPMCD), a new pattern recognition method, is introduced for the pattern recognition of PD in this study. However, the original VPMCD lacks general expression ability of the inter-relations of extracted features and inherits the shortcomings of least squares (LS) regression. To overcome the above defects, an improved VPMCD based on kernel partial LS (KPLS) regression, which is called as KPLS-VPMCD, is proposed in this study. Experiments and analyses are implemented using both UCI datasets and the extracted features of PD signals. The experiments show that the performance of the proposed KPLS-VPMCD is better than those of the existing methods such as VPMCD, back propagation neural networks, and support vector machines. The conclusion is that KPLS-VPMCD is an efficient supervised learning algorithm with consistency and good performance for PD pattern recognition.
To address the non-stationary and nonlinear characteristics of vibration signals produced by rolling bearings and the noise pollution of acquired signals, a fault diagnosis method based on singular value decomposition...
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To address the non-stationary and nonlinear characteristics of vibration signals produced by rolling bearings and the noise pollution of acquired signals, a fault diagnosis method based on singular value decomposition (SVD), empirical mode decomposition (EMD) and variable predictive model-based class discrimination (VPMCD) is proposed in this paper. VPMCD is a novel pattern recognition method;however, according to the results obtained when the fault diagnosis method is applied to a small sample, the stability of the VPM constructed based on the least squares (LS) method is not sufficient, as demonstrated by the multiple correlations found between independent variables. This paper uses the partial least squares (PLS) method instead of the LS method to estimate the model parameters of VPMCD. Compared with the back-propagation neural network (BP-NN) and least squares support vector machine (LS-SVM) methods, based on numerical examples, the method presented in this paper can effectively identify a faulty rolling bearing.
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