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Fault diagnosis method based on multi-head attention and shafting equipment periodicity

A technology for shafting equipment and fault diagnosis, applied in neural learning methods, testing of mechanical components, testing of machine/structural components, etc., can solve problems such as low degree of parallel computing and difficulty in extracting long-distance correlation information, and achieve parallelism High degree of calculation, good fault diagnosis effect, and strong long-distance information ability

Active Publication Date: 2021-06-04
HENAN UNIVERSITY OF TECHNOLOGY
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of the difficulty in extracting long-distance correlation information in traditional fault diagnosis methods, or the low degree of parallel computing, the present invention provides a fault diagnosis method based on multi-head attention and shafting equipment periodicity, which at least partially solves the above problems

Method used

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  • Fault diagnosis method based on multi-head attention and shafting equipment periodicity
  • Fault diagnosis method based on multi-head attention and shafting equipment periodicity
  • Fault diagnosis method based on multi-head attention and shafting equipment periodicity

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Embodiment 1

[0061] An embodiment of the present invention provides a fault diagnosis method based on multi-head attention and periodicity of shafting equipment, including:

[0062] S101: Collect several cycles of to-be-diagnosed samples of the shafting equipment, and perform standardized processing after adding periodic information of the shafting equipment to the to-be-diagnosed samples;

[0063] S102: Using the standardized sample data as an input of the multi-head attention fault diagnosis model to obtain a fault diagnosis result.

[0064] Specifically, the structure of the multi-head attention fault diagnosis model is as follows figure 1 As shown, the offline training process of the multi-head attention fault diagnosis model includes the following steps:

[0065] Step A1: Perform nT independent sampling on the shafting equipment through m sensors to obtain the historical data matrix X of the shafting equipment 0 , and in matrix X 0 The cycle information of shafting equipment is add...

Embodiment approach

[0075] Taking the ZHS-2 multifunctional motor flexible rotor test bench as the shafting equipment as an example, the test bench structure is as follows: figure 2 shown. In this embodiment, 8 vibration acceleration sensors installed in the horizontal direction of the rotor support base are used to collect the samples to be diagnosed on the test bench. Specifically, the samples to be diagnosed are the time-domain vibration signals of the rotor of the test bench, and the signals are passed through the HG8902 The collection box is transmitted to the host computer.

[0076] The test bench can simulate various operating modes of shafting equipment, including rotor unbalanced failure mode, ball failure mode, fan broken blade failure mode, base loose failure mode, gear broken teeth failure mode and normal operation mode, etc. Adopt seven operation modes: rotor unbalanced (1 screw), rotor unbalanced (3 screws), rotor unbalanced (5 screws), rotor unbalanced (7 screws), fan blade failu...

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Abstract

The invention provides a fault diagnosis method based on multi-head attention and shafting equipment periodicity. The method comprises the following steps: step 1, collecting to-be-diagnosed samples of the shafting equipment in a plurality of periods, adding periodic information of the shafting equipment into the to-be-diagnosed samples, and performing standardization processing; and 2, taking the standardized sample data as the input of a multi-head attention fault diagnosis model to obtain a fault diagnosis result. Aiming at the characteristics of periodicity, nonlinearity and coupling of shafting equipment vibration signals, the method integrates the periodicity characteristics of shafting equipment into time domain fault signal data, distinguishes the directionality of long-distance information by using two position codes, and has relatively strong long-distance information extraction capability and relatively high parallel calculation capability.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of shafting equipment, in particular to a fault diagnosis method based on multi-head attention and periodicity of shafting equipment. Background technique [0002] With the development of production and the modernization of science and technology, the structure of modern mechanical equipment has become more complex, and various functions have become more comprehensive. The degree of automation of mechanical equipment has also been continuously improved, and shafting equipment is an important part of it. structure. Due to the influence of many factors, shafting equipment has its service life and is prone to failure, which may lead to the reduction of its expected efficacy, stop operation, etc., and even cause more serious catastrophic accidents. Therefore, timely detection of faults and identification of fault types will not only help prolong its service life, but also effectively avoid da...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G01M13/028G01M13/045
CPCG06N3/084G01M13/028G01M13/045G06N3/044G06N3/045G06F18/24323G06F18/2415
Inventor 冯肖亮赵广闫晶晶马利吴兰
Owner HENAN UNIVERSITY OF TECHNOLOGY
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