Fault diagnosis method and device for rolling bearing of running gear of locomotive

A fault diagnosis device and technology for rolling bearings, which are applied in the fields of mechanical bearing testing, computer parts, character and pattern recognition, etc., can solve the problems of low diagnostic accuracy and long time-consuming to establish a diagnostic model, and achieve high classification accuracy and training time. The effect of short, improved speed and accuracy

Inactive Publication Date: 2015-07-29
GUANGXI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0008] In order to solve the problems in the prior art that the diagnosis accuracy of rolling bearing faults in running parts of locomotives is low and it takes a long time to establish a diag

Method used

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  • Fault diagnosis method and device for rolling bearing of running gear of locomotive
  • Fault diagnosis method and device for rolling bearing of running gear of locomotive
  • Fault diagnosis method and device for rolling bearing of running gear of locomotive

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Experimental program
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Embodiment 1

[0075] Such as figure 1 Shown is a flow chart of a method for diagnosing a rolling bearing fault in a running part of a locomotive according to an embodiment of the present invention. The method includes:

[0076] Step S101: Collect the vibration acceleration data of the rolling bearings of the running part of the locomotive under different fault types, and group the data according to the fault types. The total number of groups is A. The four different fault types include: normal signal, inner ring fault, outer ring fault Faults and rolling element failures;

[0077] The four working conditions of normal signal, inner ring fault, outer ring fault and rolling element fault represent the fault types of locomotive rolling bearings, which can be represented by 1, 2, 3, and 4 in the fault diagnosis model established by the subsequent method.

[0078] Step S102: Obtain the frequency domain signal of the vibration acceleration data according to the grouped vibration acceleration dat...

Embodiment 2

[0089] Such as figure 2 Shown is a flow chart of another method for diagnosing a rolling bearing fault in the running part of a locomotive according to an embodiment of the present invention, which is a specific description of step S102, including:

[0090] Step S201: Perform trend elimination processing on the grouped vibration acceleration data to remove noise in the vibration acceleration data;

[0091] Step S202: Perform secondary integration on the vibration acceleration data signal after the trend item is eliminated to obtain the displacement signal, that is, obtain the time domain signal after time domain analysis;

[0092] Step S203: performing fast Fourier transform on the time-domain signal to obtain a frequency-domain signal after spectrum analysis.

[0093] This embodiment is a refinement of step S102, and has all the technical effects of the first embodiment, and will not be repeated here.

Embodiment 3

[0095] Such as image 3 Shown is a flow chart of another method for diagnosing rolling bearing faults in the running part of a locomotive according to an embodiment of the present invention, which is a specific description of step S103, including:

[0096] Step S301: Perform three-layer wavelet packet decomposition on the denoised group A frequency domain signals, and obtain 8 frequency domain components f for each group of signals 1 / 8 , f 2 / 8 , f 3 / 8 , f 4 / 8 , f 5 / 8 , f 6 / 8 , f 7 / 8 , the signal of f;

[0097] Three-layer wavelet packet decomposition, that is, one-dimensional third-order fast wavelet transform, uses multiple iterations of wavelet transform to analyze the input signal, so after three wavelet transforms, as Figure 4 shown.

[0098] Step S302: Calculate the energy E of each frequency domain component of the frequency domain signal 1 / 8 ,E 2 / 8 ,E 3 / 8 ,E 4 / 8 ,E 5 / 8 ,E 6 / 8 ,E 7 / 8 ,E, construct the energy eigenvector of A*8 Xi =...

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Abstract

The invention discloses a fault diagnosis method and device for a rolling bearing of a running gear of a locomotive. The method includes the steps of collecting vibration acceleration data of the rolling bearing of the running gear of the locomotive under different fault types, and grouping the data according to the fault types; obtaining frequency domain signals of the vibration acceleration data according to the grouped vibration acceleration data; conducting three-layer wavelet packet decomposition on the frequency domain signals, and constructing fault characteristic sets; randomly arranging the fault characteristic sets, using the front B sets as the training sets, and using the rear C sets as the testing sets, wherein the sum of B and C is equal to A, and B is larger than C; training the B training sets through a Naive Bayes classifier, and establishing fault diagnosis model based on Naive Bayes for the rolling bearing of the running gear of the locomotive; classifying the C testing sets according to the fault diagnosis model, and evaluating the classification performance of the fault diagnosis model according to the classification result and through the combination with the fault characteristic sets.

Description

technical field [0001] The invention relates to the technical field of mechanical fault diagnosis, in particular to a method and device for fault diagnosis of rolling bearings in running parts of locomotives based on wavelet packets and naive Bayesian classification. Background technique [0002] The railway is an important infrastructure of the country and plays an irreplaceable role in the social and economic development and national defense of our country. In recent years, with the rapid development of my country's economic construction, the country has increased investment in railway transportation, and high-speed and heavy-duty transportation is one of the key manifestations. Speed ​​and load are two important indicators that restrict each other to evaluate the performance of trains. With the continuous increase of train speed and traction load, the safe operation of locomotives has higher requirements, and the maintenance of locomotives has also become more and more im...

Claims

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Application Information

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IPC IPC(8): G01M13/04G06K9/62G06K9/66
Inventor 贺德强陈二恒刘旗扬周继续向伟彬
Owner GUANGXI UNIV
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