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Extraction method for early failure sensitive characteristics based on ensemble empirical mode decomposition (EEMD) and wavelet packet transform

A wavelet packet transform and sensitive feature technology, which is applied in the testing of mechanical parts, the testing of machine/structural parts, measuring devices, etc., can solve the problems that self-adaptive decomposition cannot be achieved, and wavelet packet parameters cannot be automatically selected.

Inactive Publication Date: 2013-05-08
BEIJING INFORMATION SCI & TECH UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the parameters of the wavelet packet cannot be selected automatically, and adaptive decomposition cannot be realized.

Method used

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  • Extraction method for early failure sensitive characteristics based on ensemble empirical mode decomposition (EEMD) and wavelet packet transform
  • Extraction method for early failure sensitive characteristics based on ensemble empirical mode decomposition (EEMD) and wavelet packet transform
  • Extraction method for early failure sensitive characteristics based on ensemble empirical mode decomposition (EEMD) and wavelet packet transform

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0068] Embodiment 1, analyzing the simulation signal: when the rolling bearing of the main shaft system fails, the load change caused by the failure will cause the rolling bearing to show non-uniformity during the rolling process, and at the same time act on the rolling bearing in the form of alternating force . When a gear in a spindle system fails (such as pitting on the gear face), severe gear mesh frequency modulation occurs due to the asymmetry of the load on the gear mesh. The mechanical structure may also be strongly excited during the gear meshing process, resulting in a stronger modulation effect of the mechanical resonance on the gear meshing frequency. The vibration signal of a faulty gear can be modeled as:

[0069] s m ( t ) = Σ k = 1 K A k ...

Embodiment 2

[0079] Embodiment 2, rolling bearing fault diagnosis:

[0080] This embodiment adopts bearing data from a rolling bearing fault simulation test bench of the Electrical Engineering Laboratory of Case Western Reserve University in the United States. The bearing to be tested supports the motor shaft. The model of the fan end bearing is SKF6205 deep groove ball bearing, and its specific specifications are shown in Table 1.

[0081] Table 1 Specification information of deep groove ball bearings

[0082]

[0083] The rotation speed of the bearing is 1797 revolutions per minute, that is, the rotation frequency is 29.95Hz, and the sampling frequency is 12000Hz. The fault frequency is shown in Table 2.

[0084] Table 2 Fault frequency of deep groove ball bearings

[0085] Inner ring failure

Outer ring failure

cage failure

rolling element failure

162.19Hz

107.36Hz

11.93Hz

141.17Hz

[0086] The local damage of the rolling bearing is a ...

Embodiment 3

[0088] Embodiment 3, fault diagnosis of the rotor system:

[0089] The INV1612 multifunctional flexible rotor experimental system of Beijing Dongfang Vibration and Noise Technology Research Institute can perform experiments such as rotor dynamic balance, oil film whirl, friction vibration, etc. It mainly consists of two parts, the first part includes INV1612T multifunctional flexible rotor test bench and various sensors (1 vibration sensor, 1 photoelectric sensor and 2 eddy current sensors), and the second part includes INV306U acquisition and analysis system. The multi-functional flexible rotor experimental system is used to carry out experiments on normality, misalignment and rubbing of the rotor. The sampling frequency is set to 1024Hz, and the rotational speed of the rotor is 960rpm. The vibration displacement signals of the three situations of neutralization and rubbing, the length of which is 1024, the signals measured in the horizontal and vertical directions are set to...

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Abstract

The invention relates to an extraction method for early failure sensitive characteristics based on ensemble empirical mode decomposition (EEMD) and wavelet packet transform. The extraction method for the early failure sensitive characteristics based on the EEMD and the wavelet packet transform includes the following steps: (1), collected original vibration signals of mechanical and electrical equipment are decomposed according to the EEMD, white noise is added, and intrinsic mode function (IMF) components are obtained through decomposition; (2), the sensitive IMF components closely related to failure are chosen, and other irrelative IMF components are ignored; (3), the sensitive IMF components chosen through step (2) are decomposed in an orthogonal wavelet packet mode, and a wavelet coefficient of each node is obtained; and (4), envelopes are extracted from the obtained wavelet packet coefficients by adoption of the Hilbert transform and the Fourier transform, power spectrums are calculated, the power spectrum corresponding to each wavelet packet coefficient is obtained and serves as the early failure sensitive characteristic , and the sensitive characteristics are automatically obtained. Self-adapting signals can be decomposed, the sensitive characteristics can be convenient to obtain automatically, diagnosis precision and speed are improved, and a mechanical and electrical system can be diagnosed quickly, accurately and stably. The extraction method for the early failure sensitive characteristics based on the EEMD and the wavelet packet transform can be applied to the field of mechanical and electrical equipment failure diagnosis.

Description

technical field [0001] The invention relates to a method for extracting fault features of electromechanical equipment, in particular to a method for extracting early fault sensitive features based on EEMD (overall average empirical mode decomposition) and wavelet packet transform. Background technique [0002] High-speed ultra-precision high-end manufacturing equipment technology is a national key development industry. It has been widely used in aerospace, nuclear power, high-tech and other industries, greatly improving product processing accuracy and production efficiency. Spindle system includes spindle, bearing, tool handle, tool (or workpiece) and other components, and is an important subsystem of CNC machine tools. The dynamic performance of the spindle system, such as accuracy retention, safety and reliability, has become a bottleneck in the operation of the machine tool, which seriously restricts the development and practical application of high-speed ultra-precision ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/00G01M13/02G01M13/04
Inventor 王红军徐小力
Owner BEIJING INFORMATION SCI & TECH UNIV
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