Rotary machine fault intelligent diagnosis method based on fuzzy soft morphology graph recognition

A rotating machinery and graphic recognition technology, applied in the field of high-speed rotating machinery fault diagnosis and signal processing, can solve problems such as difficult knowledge rules, difficult automatic extraction of information, and insufficient use of information, so as to improve accuracy, improve diagnosis speed and Accuracy, the effect of improving the level of automatic diagnosis

Inactive Publication Date: 2020-11-17
哈尔滨雅静振动测试技术有限公司
View PDF1 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the actual fault diagnosis, because the information in the graphics, especially the information in the multi-dimensional graphics, is difficult to be automatically extracted, a large amount of information in the graphics is not fully utilized. To express it with knowledge rules, this invention proposes a method based on fuzzy soft morphology to extract the graphic features of vibration parameters and then diagnose faults, which can effectively improve the accuracy of fault diagnosis of rotating machinery

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Rotary machine fault intelligent diagnosis method based on fuzzy soft morphology graph recognition
  • Rotary machine fault intelligent diagnosis method based on fuzzy soft morphology graph recognition
  • Rotary machine fault intelligent diagnosis method based on fuzzy soft morphology graph recognition

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0063] Diagnosis graphic information has been effectively used. In the past fault diagnosis, due to the difficulty of graphic feature extraction, it is difficult to be directly used in the fault diagnosis of rotating machinery, so the diagnostic accuracy is low. The invention constructs fuzzy soft morphological parameter graphic feature extraction and preprocessing methods, which can effectively obtain vibration information and improve the accuracy of fault diagnosis.

[0064] The invention relates to the field of high-speed rotating machinery fault diagnosis technology and signal processing technology. It is the first domestic rotating machinery fault diagnosis method based on fuzzy soft morphology graphic recognition technology, and can be applied to petrochemical, metallurgy, electric power, aviation and aerospace industries. Some large rotating machinery as the main production tools such as aero-engines, gas turbines, fans, steam turbines, compressors and generators.

[00...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a rotary machine fault intelligent diagnosis method based on fuzzy soft morphology pattern recognition. The method comprises the following steps: arranging a vibration sensoron a rotating machine to be diagnosed to acquire a vibration signal, and forming a time sequence vibration signal by acquired data; generating a three-dimensional parameter graph from the acquired time sequence vibration signals; preprocessing difference reconstruction on the three-dimensional parameter graph; performing gray adaptive histogram equalization enhancement preprocessing on the graph after the difference reconstruction preprocessing; adopting a composite fuzzy soft form filter to carry out enhancement processing on the rotary machine vibration parameter graph after enhancement preprocessing; adopting a fuzzy soft morphology composite edge detection operator to perform texture feature extraction on the enhanced rotary machine vibration parameter graph; for the texture features,extracting fault features by adopting a gray primitive gradient co-occurrence matrix; and performing fault diagnosis by adopting an artificial neural network method according to the extracted fault features.

Description

technical field [0001] The invention relates to an intelligent fault diagnosis method for rotating machinery based on fuzzy soft morphological pattern recognition, and belongs to the technical field of fault diagnosis and signal processing of high-speed rotating machinery. Background technique [0002] With the continuous development of modern industrial production and the advancement of science and technology, in order to maximize production efficiency and product quality, some large rotating machinery such as aero-engines, gas turbines, fans, steam turbines, compressors and generators are constantly moving towards large, high-speed , automation, intelligence, continuous operation and complex structure, so that the damage caused by the failure of rotating machinery equipment will become more and more serious. Once a failure occurs, it will cause huge losses to the enterprise. Therefore, the operating status of rotating machinery should be monitored. Monitoring and fault dia...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06N3/08
CPCG06N3/084G06V10/44G06F2218/02G06F2218/00G06F2218/08
Inventor 刘占生
Owner 哈尔滨雅静振动测试技术有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products