Study On Identification Method Of Source Path Of Multi-correlation Vibration And Noise For Vehicle

The vibration and noise of vehicle is a most important Evaluation index of comfort and also isvery important to monitor the running state of mechanism. Generally, the testing of vehicle iscarried out in the movement process, so a testing signal may not directly reflect the sourceinformation of the corresponding position when sensor is affected by different excitation source. Itis influenced by the environment noise, test systems or interference effects of different sources tothe position, resulting in correlation between measurement signals. To analyze signal responsecontribution rate, the real source contribution rate is exaggerated or reduced. For vibration faultdiagnosis, source and path identification under multi-correlation background, the main contents ofthis paper are summarized as follows:(1) The main characteristics of Empirical mode decomposition (EMD) was discussed in thispaper. The false component and mode mixing in decompositing process was improved, which wasapplied to do denoising and extract feature of vehicle testing signal. The method of using thecoherence analysis to avoid the misjudgment of weak signal and false component was proposed.From the perspective of analyzing frequency-energy of signal components, the influence of falsecomponent generating in EMD decomposition was researched and the new regularity thatincreasing high frequency component or reducing low frequency component can reduce the ratio offalse component was discovered. By studying the EMD decomposing characteristics of white noise,the method of multiple stacking colored noise to improve mode mixing of the EMD was proposed(MH-EMD, EMD with mutil-high frequecy), which simulation effect is obviously improvedcompared to the classic Ensemble Empirical Mode Decomposition (EEMD). The system methodwith improved EMD was applied to extract signal features from rotating machinery and exhaustsystem, the new method was verified to be feasible and effective.(2) A new adapted denoising method was proposed in paper, as is the joint of ensembleempirical mode decomposition (MH-EMD) and least mean square algorithm (LMS). The algorithmperformance of the LMS with fixed step and fixed filter order, variable step and variable order werestudied, it was proposed that convergence direction of algorithm with both order and step changingin iterative process is up to the expectation of least mean square error, a kind of jointing order andstep LMS (VSVT-LMS) was developed, and Using MH-EMD, the original signal was decomposed,so that every mode component would be narrowband, and then denoising by VSVT-LMS. The instability of the LMS for wideband signals, but also the EMD threshold selection problem isavoided effectively. Simulation and vehicle signal were analyzed and it shows that the new methodhas a good adaptive characteristics and accuracy, and the feasibility of the method was verified inengineering.(3) For coupled or related characteristics of testing signal on vehicles, some separationmethods were proposed. First, the classical methods of blind source separation were discussed.Based on the comparison of the amplitude density, a blind signal separation method was proposedto the transient mixed model. If the frequency of the signal is less, the method can be used toeffectively identify the single frequency and common frequency signal. The joint approximatediagonalization method is improved, the segments joint approximate diagonalization withequalization and optimal distance matrix joint approximate diagonalization method were proposed,the improved methods can achieve higher precision. Then, using the high order section of higherorder cumulants, and combining with the approximate diagonalization and joint block diagonalmatrix, which method was used better when sensor number is less than the source number.Combining the improved wavelet subbands with the blind source separation, the separation of theobservation signal with the characteristics of statistics relevance source could be effectively solved;For the mixed model with convolution properties, comparising separation algorithms of the timedomain and frequency domain, a frequency domain algorithm based on independent judgment ofmutual information vector was proposed, nonlinear function including the separation matrix of allthe frequency band was considered to avoid the disturbance uncertainty of frequency-domaindeconvolution in the iterative process. Based on the system dynamic model of the frame, theapplication range of transient model and the convolutive model was studied and new methods wereverified.(4) The model of multiple input and single output which is good for subsystem wasestablished and derived from the theory. Then, by constrasting to calculation transfer function andreal transfer function, the analysis method of input interference direction was proposed, and therelation of the acceleration with force transfer function and acceleration with acceleration transferfunction were derived from the theory and experiment. A bench of frame system was built, basedon coherent analysis, the one-way interference model was verified, and for the vibration problem ofa commercial vehicle, the vibration characteristics of excitation and response in the transmissionpaths were tested and analyzed. Base on the method of bidirectional interference average, thecontribution of excitation source and transfer path were effectively identified, which verify the feasibility of the model and method. Others, the recognition rate and accuracy of abnormalvibration diagnosis were improved by using new method. Combined with the general transfer pathanalysis method, which can be applied to effectively identify sound source contradiction and solvethe problem of the mechanical noise for the relevant characteristic noise source on the farm vehicle.In conclusion, the characteristics of a multi correlated subsystem were systematically studiedand the issue of its separation and identification was also studied in this paper. The method ofsignal self-adaptive feature extraction, the algorithm of blind source separation for different modelsand the mathematical expressions of different interference model were proposed.These methodsare verified in the vehicle.The results have an important theoretical significance and engineeringapplications for improving fault diagnosis accuracy and vehicle NVH performance, and guidingvehicles NVH designs.