Method for extracting engineering machine running characteristic signals

A technology of operating characteristics and construction machinery, applied in the field of signal processing, can solve the problems of not considering the difference in the selection of wavelet decomposition layers, the inability to adjust the threshold of filtering effect, and "over-retaining" noise coefficient, so as to improve blindness and increase speed , the effect of improving the effect

Active Publication Date: 2013-01-09
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

This invention has the following deficiencies: [1] The number of wavelet transform decomposition layers is selected as 5, and the difference in the selection of wavelet decomposition layers for different signals is not considered, and there is no self-adaptive selection of wavelet decomposition layers according to the characteristics of the signal; [2] The selection of the wavelet threshold uses the SUREshrink method. SUREshrink is an unbiased estimation method based on the mean square error criterion. Its disadvantage is that the noise coefficient is "over-reserved", and the threshold cannot be adjusted according to the filtering effect.

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  • Method for extracting engineering machine running characteristic signals
  • Method for extracting engineering machine running characteristic signals
  • Method for extracting engineering machine running characteristic signals

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Embodiment Construction

[0029] The embodiments of the present invention are described in detail below. This embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following implementation example.

[0030] Such as figure 1 As shown, in this embodiment, a vibration signal is collected during real-time monitoring of key components of a certain type of crane truck. Use the following steps to process:

[0031] (1) The number of intercepted sampling points is 2 11 ;

[0032] (2) The wavelet basis function selects db5;

[0033] (3) Define the Kolmogorov-Smirnov test statistic as follows:

[0034] Remember F * (D) is a normal distribution cumulative function, and its mean variance Cumulative distribution function for the noise sample statistic.

[0035] definition Represents the maximum vertical distan...

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Abstract

The invention discloses a method for extracting engineering machine running characteristic signals in the technical field of signal processing, which comprises the following steps of: determining an optimal decomposition layer number of wavelet decomposition according to a wavelet coefficient whitening inspection method by acquiring a machine running state data and selecting a wavelet function; then calculating a de-noising threshold value of a wavelet coefficient of each decomposition layer, and de-noising the wavelet coefficient by adopting a soft threshold function; and performing inverse wavelet transform on the de-noised wavelet coefficient to obtain state data of the de-noised engineering machine running characteristic signals. The method improves the adaptability and computing speed, improves the signal-to-noise ratio of the acquired signals, and can meet the requirements of remote real-time monitoring, fault diagnosis and performance prediction.

Description

technical field [0001] The invention relates to a method in the technical field of signal processing, in particular to a method for extracting operating characteristic signals of construction machinery. Background technique [0002] The vibration signals of key components such as engines, pumps, and hydraulic systems of construction machinery in the field have strong noise and non-steady-state random characteristics. If the signals directly collected by the sensor do not undergo effective noise removal, it is difficult to directly perform fault diagnosis and performance prediction. early warning. [0003] Wavelet analysis technology is a powerful processing tool in the field of signal processing. Compared with Fourier analysis technology, it has good localization properties and multi-resolution characteristics in time domain and frequency domain, so it is widely used. The basic idea of ​​wavelet noise reduction is to decompose the signal into different scales by wavelet, st...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01H11/00
Inventor 李彦明杜文辽刘成良
Owner SHANGHAI JIAO TONG UNIV
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