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Sparse-decomposition-based hybrid fault feature extraction method of gear wheel and bearing

A fault feature and extraction method technology, which is applied in mechanical bearing testing, machine gear/transmission mechanism testing, mechanical component testing, etc., can solve problems such as input classification and recognition algorithm fault feature signal extraction, single damage, etc.

Active Publication Date: 2016-03-23
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0003] In recent years, scholars have proposed a variety of diagnostic methods for mixed faults, mainly including: multi-scale wavelet, empirical mode decomposition (EMD), blind source separation and pattern recognition, among which the most widely used method is pattern recognition, but The fault characteristic signal input to the classification and identification algorithm is basically extracted from an independent single damage

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  • Sparse-decomposition-based hybrid fault feature extraction method of gear wheel and bearing
  • Sparse-decomposition-based hybrid fault feature extraction method of gear wheel and bearing
  • Sparse-decomposition-based hybrid fault feature extraction method of gear wheel and bearing

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Embodiment 1

[0079] like figure 1 , this embodiment discloses a sparse decomposition-based feature extraction method for mixed gear and bearing faults, which can be used to diagnose mixed faults consisting of distributed gear faults and local gear and bearing faults in a gearbox. The specific implementation includes steps in the following order:

[0080] S1. Collect vibration acceleration signals including gear and bearing faults.

[0081] S2. Construct a stationary modulation dictionary, and optimize the atomic parameters in the dictionary atoms by using the ratio correction method, and extract the stationary modulation components in the signal by using the matching pursuit algorithm.

[0082] S3. Identify the multi-order natural frequencies and damping ratios of the gearbox including gears and bearings from the remaining signals by applying a correlation filtering method.

[0083] S4. Construct the shock modulation dictionary, segment the remaining signal, and use the matching pursuit ...

Embodiment 2

[0126] This embodiment specifically illustrates the feature extraction method for gear and bearing hybrid faults based on sparse decomposition disclosed in the present invention through a simulation test of mixed local faults of automotive transmission gears and bearings:

[0127] The tested gearbox is a three-shaft five-speed automotive manual transmission, and its structure is as follows: figure 2 shown. The mixed partial fault is composed of the inner ring fault of the rolling bearing at the output shaft end (model: NUP311EN, fault size: 0.2mm wide, 1mm deep) combined with broken teeth of the fifth-speed output gear. The experimental equipment is shown in Figure 3. Set the input shaft speed to 1000rpm, the operating parameters of the gearbox are listed in Table 1, and the structural parameters of the rolling bearing model NUP311EN are listed in Table 2.

[0128] Table 1 Experimental test gearbox operating parameters (unit: Hz)

[0129]

[0130]

[0131] Table 2 NUP...

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Abstract

The invention discloses a sparse-decomposition-based hybrid fault feature extraction method of a gear wheel and a bearing, wherein the method can be used for diagnosing a hybrid fault formed by a distributed gear wheel fault and a local gear wheel and bearing fault in a gear case. When a steady modulation dictionary is constructed, atomic parameter optimization is carried out by using a discrete frequency spectrum correction technology, thereby improving precision of steady modulation component separation. When an impact modulation dictionary is constructed, an over-complete dictionary using a multi-stage inherent-frequency unit impulse response function as an atom is established and the inherent frequency and the damping ratio are identified in a self-adapting mode from a fault vibration signal, so that an impact response waveform caused by local faults of the gear wheel and the bearing can be represented well. After optimization of the steady modulation dictionary and the impact modulation dictionary, the dictionary redundancy is substantially reduce; and with a segmented matching tracking method, the point number of inner product calculation during the sparse coefficient solving process is reduced. On the basis of the two kinds of measures, the speed of signal sparse decomposition is improved.

Description

technical field [0001] The present invention relates to the fields of rotating machinery and signal processing, in particular to a method for extracting mixed fault features of gears and bearings based on sparse decomposition, mainly including sparse dictionary optimization technology based on correlation filtering method and sparse coefficient solving technology based on matching pursuit algorithm. Background technique [0002] In actual engineering, manufacturing or installation errors are unavoidable in gearboxes, and distributed faults (tooth profile errors, gear wear, shaft misalignment, and shaft imbalance, etc.) are also prone to occur after long-term use, resulting in the amplitude of meshing vibration of gears. value modulating signal. When the gears have local faults (pitting corrosion, spalling and broken teeth, etc.), the interaction of the gear meshing pairs will generate regular transient impact forces, which will excite the natural frequencies of the gears, sh...

Claims

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Application Information

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IPC IPC(8): G01M13/02G01M13/04
CPCG01M13/021G01M13/04
Inventor 李永焯丁康何国林杨志坚林慧斌陈祝云
Owner SOUTH CHINA UNIV OF TECH
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