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Kernel regression decomposition method and system for rolling bearing fault diagnosis

A fault diagnosis system and rolling bearing technology, applied in the testing of mechanical components, testing of machine/structural components, instruments, etc., can solve the problems of destroying the physical meaning of IMF, confusing time-frequency distribution, etc., and achieve high speed and good engineering application effect. , the effect of simple operation

Active Publication Date: 2019-06-11
WENZHOU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the working environment, how to correctly choose the amplitude of the added noise still needs further research. The frequent occurrence of modal aliasing is also one of the main shortcomings of EMD. The modal aliasing is caused by signal interruption, and interruption is an indefinite form of disturbance. Signal, which is often encountered in actual processing
Disruptions can lead to confusing time-frequency distributions, which in turn destroy the physical meaning of the IMF

Method used

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  • Kernel regression decomposition method and system for rolling bearing fault diagnosis
  • Kernel regression decomposition method and system for rolling bearing fault diagnosis
  • Kernel regression decomposition method and system for rolling bearing fault diagnosis

Examples

Experimental program
Comparison scheme
Effect test

example example 1

[0072] Such as image 3 Shown is the result map processed by the rolling bearing fault diagnosis system. According to the existing bearing information, the fault frequency of the outer ring of the bearing in this experiment can be calculated as 91.15 Hz, and the fault frequency can be quickly identified as 87.5 Hz from the figure. Hz is close to the theoretical value, of which 29.8Hz is the rotation frequency of the motor, and it can be determined that the fault type of this rolling bearing is the fault of the outer ring of the bearing.

Embodiment example 2

[0074] On the basis of Example 1, this implementation case 2 will further deal with more complex bearing faults. By calculating the faulty bearing in this experiment is a composite fault including bearing inner ring, outer ring and rolling element faults. Through the calculation of the data of the test bearing, the fault frequency of the inner ring is 197.05Hz, the fault frequency of the outer ring is 121.51Hz, and the fault frequency of the rolling element is 79.25Hz. from Figure 4 It can be concluded that the frequency of 120Hz matches the outer ring fault, 160Hz matches the double frequency of the rolling element fault, and 202.5Hz basically matches the inner ring fault. Thereby it is further illustrated that the present invention has a better treatment effect and is worthy of popularization and application.

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Abstract

The invention discloses a kernel regression decomposition method for rolling bearing fault diagnosis and a system thereof. The system comprises a motor, a belt transmission shaft, a shaft coupling, a testing bearing, an accelerometer, a multichannel data acquisition analyzer and a computer. A faulted bearing is connected through the motor, the belt transmission shaft and the shaft coupling. The accelerometer is fixed on a bearing seat of the tested bearing. The output end of the accelerometer is connected with the multichannel data acquisition analyzer. Data which are extracted by the analyzer are stored and transmitted to the computer. Furthermore signal data are analyzed on the computer according to the kernel regression decomposition method, thereby identifying a composite fault of the bearing, and realizing accurate testing for the operation state of the rolling bearing. The kernel regression decomposition method and the system thereof realize effective acquisition for fault information of the rolling bearing and furthermore have advantages of high transmission performance, high accuracy, high speed, simple operation and high engineering application value, etc.

Description

technical field [0001] The invention relates to the field of fault diagnosis of mechanical equipment, in particular to a kernel regression decomposition method and system for fault diagnosis of rolling bearings. Background technique [0002] Rolling bearings are important mechanical components widely used in various machines, and are also one of the most easily damaged components in machines. Rotating machinery equipment is entirely dependent on the health of rolling element bearings, accounting for almost 40-50% of equipment failures. Bearing failures can be extremely serious, and may lead to the shutdown of the entire production line, and even cause casualties. At this stage, the fault diagnosis of bearings mainly judges the operating status of bearings through manual experience or instruments. However, in the actual acquisition of vibration signals, it is often difficult to obtain vibration signals due to complex working conditions, high environmental noise, and long-ter...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M13/045
CPCG01M13/045
Inventor 向家伟楼凯钟永腾
Owner WENZHOU UNIV