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

Metro vehicle oil intelligent monitoring method based on dynamic adaptive trend analysis and judgment model

A dynamic self-adaptation and trend analysis technology, applied in the field of rail transit, can solve problems such as inability to make correct judgments on oil products, insufficient foresight of problems, and long detection cycle, so as to reduce oil consumption costs and improve diagnosis and prediction capabilities Effect

Pending Publication Date: 2021-06-25
上海伽易信息技术有限公司
View PDF9 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

One is that the detection cycle is long, and the other is that due to the existence of unpredictable factors in the process of equipment use, the oil change cycle is usually relatively conservative. A large number of oil products are replaced when they are still in good condition, while a few equipment is abnormal However, it cannot be dealt with in time, and it cannot make a correct judgment on the oil product in combination with the use of the equipment. In this way, the forward-looking problem of oil quality deterioration caused by abnormal conditions during the use of lubricating oil is insufficient.

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
  • Metro vehicle oil intelligent monitoring method based on dynamic adaptive trend analysis and judgment model
  • Metro vehicle oil intelligent monitoring method based on dynamic adaptive trend analysis and judgment model
  • Metro vehicle oil intelligent monitoring method based on dynamic adaptive trend analysis and judgment model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0095] A method for intelligent monitoring of subway vehicle oil based on dynamic self-adaptive trend analysis, comprising the following steps:

[0096] Step 1: Take a representative oil sample from the subway vehicle oil;

[0097] Step 2: Use oil detection equipment to conduct on-site detection of subway vehicle oil samples;

[0098] Step 3: Based on the dynamic adaptive trend analysis, it is used to reflect the wear trend of the equipment and the change trend of the lubrication state;

[0099] Step 4: Output the oil monitoring results of subway vehicles, and diagnose and predict the hidden troubles of subway vehicles.

[0100] The present invention uses a vacuum pump to sample oil, comprising the following steps:

[0101] 1. Insert one end of the sampling tube into the oil sample gun, that is, the vacuum pump, so that the head of the sampling tube exceeds the surface of the vacuum pump by 1 cm;

[0102] 2. Remove the cap of the oil sample bottle, and install the oil sampl...

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 discloses a metro vehicle oil intelligent monitoring method based on dynamic adaptive trend analysis and a judgment model. The metro vehicle oil intelligent monitoring method comprises the following steps of 1, taking a representative oil sample from metro vehicle oil, 2, performing field detection on the metro vehicle oil sample by using oil detection equipment, 3, analyzing the wear trend for reflecting the equipment and the change trend of the lubrication state based on the dynamic self-adaptive trend, and 4, outputting a metro vehicle oil monitoring result, and diagnosing and predicting the fault hidden danger of the metro vehicle.

Description

technical field [0001] The invention relates to a subway vehicle oil monitoring method, in particular to a subway vehicle oil monitoring method and a judgment model based on dynamic self-adaptive trend analysis, and the invention belongs to the field of rail transit. Background technique [0002] At the end of the vehicle operation cycle in the subway industry, there are many lines, so the number of vehicles in operation is large, and the amount of lubricating oil required is large. As the foundation and technical support of the lubrication management system, oil monitoring technology can ensure that subway vehicles are always in a good lubrication state, avoid equipment wear caused by lubrication failure, effectively improve the trouble-free running time of equipment and extend the operating life of equipment. [0003] Chinese patent "A Gearbox Monitoring Equipment for Rail Vehicles" (application number 201821650810.1, publication number 208816640U), this utility model pate...

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): G01N33/28
CPCG01N33/2888
Inventor 李宏宇
Owner 上海伽易信息技术有限公司
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

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com