Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Buffer balance valve fault diagnosis method based on full-connection neural network

A fault diagnosis, neural network technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problems of loss of valuable information, neglect of relevance, low diagnostic accuracy, etc., to achieve strong identification and classification capabilities, The effect of strong nonlinear mapping capabilities

Pending Publication Date: 2022-07-15
YANSHAN UNIV
View PDF13 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this method tends to converge to a local minimum, loses a lot of valuable information, ignores the correlation between the whole and the local, lacks certain stability, and has low diagnostic accuracy
[0006] It can be seen that the existing hydraulic valve fault diagnosis methods cannot achieve high performance, fast and stable fault diagnosis

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
  • Buffer balance valve fault diagnosis method based on full-connection neural network
  • Buffer balance valve fault diagnosis method based on full-connection neural network
  • Buffer balance valve fault diagnosis method based on full-connection neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0052] In order to make those skilled in the art better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only Embodiments are part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] It should be noted that the terms "first", "second" and the like in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used may be interchanged under appropriate ...

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 provides a buffer balance valve fault diagnosis method based on a full-connection neural network, and relates to the technical field of fault diagnosis. The method comprises the following steps: connecting a to-be-diagnosed buffer balance valve to an oil path of a test bed, and respectively measuring the pressure of a port B and the pressure of a port T through a pressure sensor in a normal state, a wear state and a clamping stagnation state; preprocessing the obtained pressure difference data of the port B and the port T to form a data set for model training; constructing a full-connection neural network model, and training the pressure data in the data set through the full-connection neural network model; adjusting the parameters and the structure of the full-connection neural network model according to the fault classification precision so as to obtain the full-connection neural network model with the optimal local network structure; and inputting pressure data of the to-be-diagnosed buffer balance valve into the trained full-connection neural network model to obtain a fault diagnosis result of the to-be-diagnosed buffer balance valve. The diagnosis method has the advantages of rapidness, stability and accuracy.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a fault diagnosis method for a buffer balance valve based on a fully connected neural network. Background technique [0002] Rotary drilling rig is a large-scale construction machinery with a high degree of electromechanical and hydraulic integration. Its main structure includes a walking system, a working system and a rotary system. The rotary system is one of the main structures of the rotary drilling rig, and the buffer balance valve is an important part of the rotary system. The working environment of rotary drilling rig is harsh, and there are often large particles of pollutants in the oil, which cause rapid wear of the balance valve or jam failure. High-pressure fluid with contaminating particles can cause serious damage to various components when it flows through the hydraulic system. Among them, the valve core of the buffer balance valve will wear quickly under ...

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
IPC IPC(8): G01M13/003G01L13/00G06K9/62G06N3/04G06N3/08
CPCG01M13/003G01L13/00G06N3/08G06N3/045G06F18/2415
Inventor 艾超徐俊高伟王磊许文强姜继尚郑鹏飞闻岩
Owner YANSHAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products