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A Wavelet Threshold Noise Reduction Method Based on Hierarchical Adaptive Threshold Function

A technology of adaptive threshold and wavelet threshold, which is applied in the fields of instrument, calculation, character and pattern recognition, etc., can solve the problems of oscillation of the reconstructed signal and deviation of the reconstructed signal, and achieve the effect of improving the accuracy rate.

Active Publication Date: 2021-03-16
BEIJING UNIV OF TECH
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Problems solved by technology

[0003] In the threshold algorithm, the hard threshold function and soft threshold function are the most common denoising functions, but there are certain limitations. The hard threshold function has discontinuities, and the reconstructed signal will oscillate. The soft threshold signal is also sensitive to low frequency bands. is compressed so that the reconstructed signal is always biased, a function such as figure 1 shown

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  • A Wavelet Threshold Noise Reduction Method Based on Hierarchical Adaptive Threshold Function
  • A Wavelet Threshold Noise Reduction Method Based on Hierarchical Adaptive Threshold Function
  • A Wavelet Threshold Noise Reduction Method Based on Hierarchical Adaptive Threshold Function

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Embodiment Construction

[0052] Rolling bearings are one of the important parts of rotating machinery, and they are also the most prone to failures. The failures are mainly divided into outer ring failures, inner ring failures and rolling element failures. The MFS mechanical fault comprehensive simulation test bench is the best tool for learning and researching mechanical faults. It can simulate common faults of mechanical equipment and study the characteristics of mechanical equipment without affecting the output and benefits. The experimental bench is as follows: image 3 shown.

[0053] In this experiment, the bearing fault data of the MFS mechanical fault comprehensive simulation test bench is used as the data source. Sampling frequency f s =2.56kHZ, the rotation frequency is 30hz, and the sampling number N is 4000.

[0054] Such as figure 2 As shown, a method for noise reduction of bearing fault signals based on hierarchical adaptive wavelet threshold function includes the following steps:

[...

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Abstract

The invention discloses a wavelet threshold noise reduction method based on a hierarchical self-adaptive threshold function. In the process of collecting bearing signals of rotating machinery, due to the interference of field equipment and the environment, the collected signals contain noise. In order to ensure the authenticity and effectiveness of the measurement data , it is necessary to denoise the collected original bearing signal. The present invention constructs a threshold function that is continuous at the threshold and is derivable in the wavelet domain and has trend parameters. By calculating the entropy values ​​of the noise signal energy and the total signal energy of each wavelet decomposition layer, the trend parameters of each layer are obtained, and the corresponding wavelet decomposition is obtained. Layer's threshold function mathematical model. The method of the invention enables the wavelet threshold function to self-adaptively select trend parameters in each decomposition layer, and can more effectively remove noise components in signals of bearing outer ring faults, inner ring faults and ball faults, and achieve better noise removal effects.

Description

technical field [0001] The invention belongs to the technical field of noise reduction of mechanical bearing fault signals, in particular to a wavelet threshold signal-to-noise separation method based on a layered adaptive threshold function for bearing fault signals. Background technique [0002] In the process of collecting bearing signals of rotating machinery, due to the interference of field equipment and the environment, the collected signals contain noise, which will have a greater impact when the equipment is faulty, which is not conducive to fault diagnosis. In order to ensure the authenticity and effectiveness of the measurement data, it is necessary to perform noise reduction processing on the collected original bearing signals. Because wavelet transform has localization characteristics in time domain and frequency domain, its multi-resolution feature is good at dealing with non-stationary signals, and it has achieved very good results in the field of denoising. ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00
CPCG06F2218/06
Inventor 王普李天垚高学金高慧慧
Owner BEIJING UNIV OF TECH