Acoustic emission source identification based on harmonic wavelet packet and support vector machine

In order to solve the fatigue damage identification problem of helicopter moving components, a new approach for acoustic emission （AE） source type identification based on the harmonic wavelet packet （HWPT） feature extraction and the hierarchy support vector machine （H-SVM） classifier is proposed. After a four-level decomposition of the HWPT, the energy feature of AE signals in different frequency bands is extracted, which overcomes the shortcomings of the traditional wavelet packet including energy leakage, and inflexible frequency band selection and different frequency resolutions on different levels. The H-SVM classifier is trained with a subset of the experimental data for known AE source types and tested using the remaining set of data. The results of pressure-off experiments on the specimens of carbon fiber materials indicate that the proposed approach can effectively implement the AE source type identification, and has a better performance in terms of computational efficiency and identification accuracy than the wavelet packet （WPT） feature extraction.

An efficient equivariant adaptive separation via independence algorithm for acoustical source separation and identification

To balance the convergence rate and steadystate error of blind source separation(BSS) algorithms, an efficient equivariant adaptive separation via independence(Efficient EASI) algorithm is proposed based on separating indicator, which was derived from the convergence condition of EASI, and can be used to evaluate the separation degree of separated signals. Furthermore, a nonlinear monotone increasing function between suitable step sizes and separating indicator is constructed to adaptively adjust step sizes, and forgetting factor is employed to weaken effects of data at the initial stage. Numerical case studies and experimental studies on a test bed with shell structures are provided to validate the efficiency improvement of the proposed method. This study can benefit for vibration & acoustic monitoring and control, and machinery condition monitoring and fault diagnosis.

Research on acoustic holography method for identification of sound sources

This paper presents the method named acoustic holography which can be used to identify noise sources. A new formula of holography reconstruction is obtained, based on the Kirchhoff integral formula. Some simulating tests are carried out using the new formula. The comparison with other reconstruction formulas proves that the new formula is more effective. By using acoustic holography method, some interesting results about the noise of a vehicle are shown. The results proves that acoustic holography is an effective method for the identification of the complex noise sources.

Equivalent source method for identification of panel acoustic contribution in irregular enclosed sound field

For the interior sound field formed by the complex vibrating structure, an identifi- cation approach of panel acoustic contribution based on equivalent source method （ESM） was presented. The normal velocity on the surface of vibrating structure was first reconstructed by using interior nearfield acoustic holography based on ESM and the prediction of whole interior enclosed sound field was realized. Then the sound pressure produced by each panel at the interested field point was respectively replaced by the radiated pressure of the enclosed interior sound field which is formed by the equivalent virtual sources located near the surface of the cav- ity. Combining with the reconstructed normal surface velocity, the acoustic contribution of each panel to any position in the cavity was obtained by transforming the complex enclosed non-free field into the simple interior free field. Numerical simulations and experiments are conducted, and the influences of the number of the equivalent sources and the distance between them and the reconstructed surface have been investigated. The results show that the proposed method is easier to be implemented with the same accuracy than the traditional analysis method.

An investigation into high-speed train interior noise with operational transfer path analysis method

Passengers’demands for riding comfort have been getting higher and higher as the high-speed railway develops.Scientific methods to analyze the interior noise of the high-speed train are needed and the operational transfer path analysis(OTPA)method provides a theoretical basis and guidance for the noise control of the train and overcomes the shortcomings of the traditional method,which has high test efficiency and can be carried out during the working state of the targeted machine.The OTPA model is established from the aspects of“path reference point-target point”and“sound source reference point-target point”.As for the mechanism of the noise transmission path,an assumption is made that the direct sound propagation is ignored,and the symmetric sound source and the symmetric path are merged.Using the operational test data and the OTPA method,combined with the results of spherical array sound source identification,the path contribution and sound source contribution of the interior noise are analyzed,respectively,from aspects of the total value and spectrum.The results show that the OTPA conforms to the calculation results of the spherical array sound source identification.At low speed,the contribution of the floor path and the contribution of the bogie sources are dominant.When the speed is greater than 300 km/h,the contribution of the roof path is dominant.Moreover,for the carriage with a pantograph,the lifted pantograph is an obvious source.The noise from the exterior sources of the train transfer into the interior mainly through the form of structural excitation,and the contribution of air excitation is non-significant.Certain analyses of train parts provide guides for the interior noise control.