Engine noise source identification method

Abstract

The invention relates to an engine noise source identification method. The method includes the following steps that: a noise diagnosis hierarchical analysis tree is constructed; the surface vibration signals of different components and corresponding engine surrounding noise signals are acquired through a vibration noise test; the noise signals are decomposed into different IMF components through an EEMD method, the IMF components are divided into different frequency bands according to the distribution of energy in the time-frequency spectra of the IMF components; based on the vibration signals of the different components and the IMF components of different frequency bands in the noise signals, calculation is carried out with a coherent power spectrum analysis method so as to obtain input coherent power spectra, and different input coherent power spectra are corresponding to different diesel engine components; paired comparative judgment matrixes are built according to the numerical values of the different input coherent power spectra, and the weights of the different components are calculated, and the weights of the different frequency bands are obtained; and different combined weight matrices are constructed according to the weights of the different components and the weights of the different frequency bands.

Description

technical field [0001] The invention relates to a method for identifying an engine noise source. Background technique [0002] As a necessary prerequisite for engine noise reduction, many noise source identification methods have been applied in research and engineering practice; there are many types of noise source identification methods, and the identification accuracy and test cost are also different; traditional noise source identification methods are relatively mature, but Relatively common, lacking in recognition accuracy and systematicness. The noise source identification method based on acoustic array technology has great advantages in terms of visibility and intuition, but this technology requires the support of relatively expensive test equipment, and it is slightly insufficient in the analysis of frequency domain results. Therefore, noise source identification methods based on signal processing have been developed rapidly and widely used in related fields. For th...

AI Technical Summary

Problems solved by technology

Compared with traditional single signal processing methods, these methods have improved the accuracy of noise source identification, but there are still some shortcomings

These methods often measure and analyze a single noise at a single rotational speed. However, the noise source identification based on a single rotational speed and a single noise measurement point cannot meet the overall accuracy. The noise source identification method based on independent components and blind source separation can only pass Professional experience identifies some noise sources, and the contribution of noise sources cannot be obtained

Noise source identification based on AHP requires professional evaluation, and the result is greatly affected by the evaluator's subjectivity

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