| With the rapid development of China’s economy and urbanization,people’s requirement for convenience and comfort traveling is in great demand.The high-speed railway and the urban rail transit play significant roles in people’s daily life.The construction of rail transit is meant to enhance people’s living standards,ease the pressure on urban traffic,maintain national economic growth,as well as adjust the relevant industrial structure.The majority of Chinese high-speed and subway vehicles apply electric traction;the electric power is generally acquired through the sliding contact between pantograph on the roof of vehicle and catenary.The pantograph,as a major part of power transmission,is undoubtedly one of the critical components of vehicle system.With the raising of vehicle’s speed,the pantograph faces more and more reliability chanllenge.The mechanical and electrical components fail occasionally,which seriously affects the stability and safety of vehicle.Therefore,it is significant to study the methods and techniques to ensure the reliable of the pantograph.However,the fault diagnosis for pantograph is still at the initial stage,which is far from reaching the demand of fast-growing vehicle in China.Therefore,aiming at the main problems encountered in the reliable and stable operation of pantograph,this paper starts from the research of pantograph fault diagnosis method.(1)Aiming at the feature values extraction problem that refers in the traditional ensemble empirical mode decomposition(EEMD),the white noise amplitude coefficient and its total average times cannot be effectively selected according to the signal characteristics.Therefore,an EEMD adaptive parameter selection algorithm is proposed.This method analyzes the different white noise amplitude parameters to work out the distribution influences on extremum points of the signal.This method can adaptively select the most effective white noise amplitude,which allows to optimize the distribution of extremum points.By analyzing the simulation signals,it is proved that the method not only can effectively reduce the modal aliasing under the premise,not reducing decomposition accuracy,but also do save the calculation time and improve the decomposition efficiency.(2)Based on improved EEMD decomposition algorithm,the decomposition of the intrinsic mode function(IMFs)of reconstructed phase space components,according to the signal characteristics of each component,using C-C algorithm to select the embedding dimension of phase space reconstruction process and time delay parameters,using the adaptive maximum likelihood estimation of intrinsic dimension evaluation method of IMFs phase space signal reconstruction,finally using the local tangent space manifold learning algorithm,high dimensional reconstruction of the signal with noise in the frequency space of phase space in low dimensional mapping,extraction of effective components which contains low dimensional space of the corresponding signal,to achieve a successful separation of signal and noise.Compared with other noise reduction methods,the results indicate that the proposed method has some advantages and can be applied to the noise reduction processing of the pantograph’s vibration signal.(3)Construction of improved EEMD decomposition and second generation wavelet decomposition of the pantograph vibration signal model based on information entropy,definition of the improved EEMD energy entropy,improved EEMD singular entropy,improved EEMD permutation entropy,improved EEMD approximate entropy,improved EEMD sample entropy,improved EEMD fuzzy entropy,the pantograph vibration signal characteristics,the information entropy of various parameters in the process of calculating optimization selection,puts forward on the pantograph vibration signal information entropy feature extraction model,through the fault characteristics and particle swarm optimization support vector machines(PSO-SVM)on the expansion analysis of pantograph fault identification,inquiry for the extraction type measuring points and fault sensitive of pantograph,and verify the modern time.The frequency analysis algorithm and the information entropy combined diagnosis method is feasible and effective in the feature extraction of the pantograph fault vibration signal.(4)According to the entropy feature based on the improved EEMD extraction algorithm,a pantograph pipe jacking is sensitive to the vibration signal of the pantograph bracket crack fault form,at present,the pantograph slipper vibration signal correlation entropy features for the pantograph fault isolation is poor,cannot satisfy the actual demands of fault diagnosis.Therefore,aiming at the characteristics of carbon slider vibration signal,second generational wavelet decomposition and information entropy feature extraction method are studied,which improves the fault recognition rate of carbon slide vertical vibration data greatly,also proves the effectiveness of the method on fault feature extraction of carbon slide plate vibration data.(5)Aiming at the problems of low accuracy,redundant diagnosis feature,timeconsuming classification and recognition in the pantograph diagnosis process,a new pantograph fault diagnosis model based on feature selection and feature dimension reduction is proposed.This model firstly extracting the characteristics information entropy of multipantograph vibration signal in time and frequency domain,then set to high dimensional feature space,using the Relief F algorithm,the distance evaluation index and joint mutual information(JMI)algorithm respectively ranking results of their respective features,will be the result for this sort of information fusion,form a set of characteristics of multiple feature ranking criteria;carry on the selection of the highest recognition accuracy of the feature dimension,then the manifold learning dimensionality analysis will analyze again,Therefore,greatly reduce the feature dimension,improve the diagnosis accuracy and calculation speed;the pantograph measured data analysis results validate the analysis model. |
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