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Study On The Main Character Distinguish Of Suspension System Of High-Speed Train

Posted on:2016-05-03Degree:MasterType:Thesis
Country:ChinaCandidate:Q L LiuFull Text:PDF
GTID:2272330461969356Subject:Vehicle Engineering
Abstract/Summary:PDF Full Text Request
With the EMU proportion of rail transport is growing rapidly,the EMU routine mainte-nance and repair require higher and higher. In the complex excitation of railway environ-ment,EMU vehicles also receive more various impact.Under a long term of running,the bur-den of the EMU suspension system and the maintenance costs have gone higher too.If a fail-ure occurs during operation,it is unclearly to identify the failure component without decom-position detection which is difficult during a routine maintenance.The EMU suspension sys-tem is key to absorb shock and transmit vibration.When it comes to failure,it is likely to en-danger the safety and stability of the vehicle.It might make passengers fell discomfort and nausea,increase the fatigue of the journey. Seriously it could cause series of failure,jeopardize the safety and stability of the vehicle and endanger passenger’s life.Therefore,in order to study the identification of suspension system failure,first of all we should study the impact from suspension system failure to vehicle dynamics.Secondly we should analysis and dig deeper on carbody and frame vibration signal to findout the features of identification.This paper summarizes the existing suspension components failure modes, causes fail-ure to analyze the impact of failures and manifestations in multi-body dynamics model. Based on SIMPACK software to establish a high-speed motor 350km/h speed level EMU model, the simulation shows the accuracy of the model. The establishment of a high-speed EMU suspension system after the failure of the model and are classified according to the fault component contrast, the degree of fault and fault combinations of simulation data. Re-sults show that:(1) EMU suspension parameters strong redundancy in component failure’s non-severely cases, the vehicle will not run dynamic performance particularly serious. The fault offen have critical degree. When the degree beyond this value,the vehicle dynamics performance will deteriorate rapidly.(2) Axle box springs, shock absorbers vertical impact of node failures tumbler wheel-rail relationship largest wheel-rail vehicle relationship is often a matter of safety, should be suf-ficient attention. Observation frame amplitude spectrum, according to the vibration of the form of the frame changes, the mobile generates and resonance frequency, can identify such failures.(3) Lateral damper, yaw damper and air spring for smooth operation of the vehicle the greatest impact, body horizontal, vertical acceleration, and shook his head angle change is also very obvious. Observed changes of the vehicle body vibration and acceleration maxi- mum value of the amplitude spectrum, can identify such component failure.After that,decomposition of carbody and frame vibration signals with Ensemble Empir-ical Mode Decomposition(EEMD),each signal decomposed into a series of IMF signals which represent the original signal’s time-frequency features.Then take a view of infor-mation entropy theory,knowing the principle and physical representitavives of energy entro-py,time entropy,time-frequency entropy,singular entropy and average entropy these five in-formation entropies,and calculate the five entropies of the IMF signals.Based on greater fluctuated entropy,set the energy entropy as x-axis,singular entropy as the y-axis and average entropy as the z-axis to establish a information entropy coordinate.According to the various failure of suspension system,each point in the entropy coordinate represents one type of fail-ure.By marking different colors to different failure compositions,we could get a map of fail-ure distribution in the coordinate system. And from the map of distribution we could identify failure type derictly.
Keywords/Search Tags:High speed EMU, suspension system, fault feature, Ensemble Empirical Mode Decomposition, EEMD, information entropy, fault identification
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