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Rotating machine bearing fault diagnosis method

A technology for rotating machinery and fault diagnosis, which is used in mechanical bearing testing, mechanical component testing, and machine/structural component testing. , calculate the simple effect

Active Publication Date: 2017-12-26
JIANGXI UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of the above situation, it is necessary to provide a fault diagnosis method for rotating machinery bearings in view of the low reliability and accuracy of rotating machinery bearing fault detection in the prior art

Method used

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  • Rotating machine bearing fault diagnosis method

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Embodiment 1

[0044] see figure 1 The method for diagnosing a bearing fault of a rotating machine in the first embodiment of the present invention includes steps S11 to S14.

[0045] Step S11 , collecting in real time the vibration signals of the bearings of the rotating machinery within a preset period of time in the operating state to obtain data samples to be detected.

[0046] Generally, a vibration signal cannot be used for fault diagnosis and analysis, and a vibration signal alone cannot accurately determine the fault of a rotating machinery bearing. Therefore, in the above steps, the vibration signal of the rotating mechanical bearing in the operating state within a preset time period is collected in real time, and the vibration signal of the rotating mechanical bearing collected in each preset time period is used as a data sample to be detected. For example, the vibration signal collected every 2s can be used as a data sample to be detected. A data sample to be detected includes m...

Embodiment 2

[0079] In order to further understand the technical solution of the present invention, a specific embodiment will be used to describe below, including steps S1-S6:

[0080] Step S1: Collect vibration signals of rotating machinery bearings under normal working conditions, as shown in the attached Figure 3a~3c , get 3 standard data samples;

[0081] Step S2: The standard data sample {x i |i=1,2,...,n} Group according to the first step length cellmax to get multiple vibration signal analysis samples X (k) ;

[0082] Step S3: Calculate the standard data samples X under different scales δ (k) Total number of boxes N δ (such as formula 1); and then calculate the box dimension Dim k B (such as formula 2), the box dimension sequence {Dim k B |k=1,2,...,ceil(n / cellmax)};

[0083] Step S4: Divide {Dim k B |k=1,2,...,ceil(n / cellmax)} grouping, calculate box dimension sequence {Dim k B}'s box dimension average D (t) and coefficient of variation C.V (t) ). The calculation ...

Embodiment 3

[0090] see Figure 4 , is the rotating machinery bearing fault diagnosis method in the third embodiment of the present invention, which further includes steps S21-S26 on the basis of the first embodiment.

[0091] During specific implementation, vibration signals of rotating mechanical bearings in various fault states can be collected in advance to obtain multiple fault state data samples. Multiple fault state data samples are analyzed according to the fractal dimension algorithm, which calculates the box dimension and variation coefficient of each fault state data sample, that is, the fault box dimension and fault variation coefficient are obtained. In this embodiment, a fault state data sample is used for illustration.

[0092] Step S21 , calculating the difference between the average box dimension and variation coefficient of the data sample to be detected and the fault box dimension and fault variation coefficient, respectively, to obtain two second calculated values. Th...

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Abstract

The embodiment of the invention provides a rotating machine bearing fault diagnosis method, which includes steps of collecting vibration signal of a rotating machine bearing under a current running state, and using multiple vibration signals within a preset time period as to-be-detected data samples; grouping the to-be-detected data samples and calculating the box dimension number of every group of data according to a fractal dimension algorithm; calculating the average value and the variable coefficient of the box dimension number; comparing the average value of the box dimension number and the variation coefficient of the to-be-detected data samples with the standard box dimension number and the standard variation coefficient, so as to determine the state of the rotating machine bearing, wherein the standard box dimension number and the standard variation coefficient are acquired through calculating the vibrating signal of the rotating machine bearing under the normal running state. The rotating machine bearing fault diagnosis method applies the box dimension number and the variation coefficient as the feature vectors to describe the bearing fault state; the model description and zone calculation are simple, and the breaking capacity is strong; the fault can be accurately monitored; therefore, the reliability is high.

Description

technical field [0001] The invention relates to the field of mechanical faults, in particular to a fault diagnosis method for rotating mechanical bearings. Background technique [0002] Rotary machinery bearings are key components of rotating machinery, and their working condition will directly affect the working condition of the entire mechanical equipment. Rotating machinery bearing failure is one of the main reasons for the failure of rotating machinery equipment, and it may even lead to major property losses in severe cases. Therefore, in order to avoid mechanical failure of rotating machinery bearings and reduce economic losses, it is very necessary to monitor the condition of bearings to ensure their normal operation. [0003] The diagnosis methods of bearing fault state mainly include temperature method, oil sample analysis method and vibration method. Since the running state of rolling bearings is often directly reflected in the vibration signal, and the vibration ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/04
CPCG01M13/045
Inventor 魏晖
Owner JIANGXI UNIV OF TECH
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