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

Fault diagnosis method for planetary gear box based on data-driven quantitative characteristic multi-granularities

A planetary gearbox, data-driven technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problem of incomplete fault diagnosis information of planetary gearboxes, achieve clear pattern recognition strategies, reduce required data, Effect of Accurate Fault Diagnosis Results

Inactive Publication Date: 2018-03-06
HARBIN UNIV OF SCI & TECH
View PDF6 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, various reasons such as sensor failure, communication delay or data discretization will lead to incomplete fault diagnosis information of planetary gearboxes, which brings great challenges to the application of data-driven methods.

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
  • Fault diagnosis method for planetary gear box based on data-driven quantitative characteristic multi-granularities
  • Fault diagnosis method for planetary gear box based on data-driven quantitative characteristic multi-granularities
  • Fault diagnosis method for planetary gear box based on data-driven quantitative characteristic multi-granularities

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach 1

[0030] Specific implementation mode one: combine figure 1 Describe this embodiment, the multi-granularity planetary gearbox fault diagnosis method based on data-driven quantitative features, is characterized in that the method includes the following steps:

[0031] Step 1. According to the collected characteristic signals of typical faulty planetary gearboxes, extract fault diagnosis features and establish an incomplete fault diagnosis information system;

[0032] Step 2: Analyze the incomplete fault diagnosis information system by using the data-driven quantitative feature relationship, calculate the feature similarity between all instances, and obtain a feature set that satisfies the data-driven quantitative feature relationship;

[0033] Step 3. Using the attribute reduction algorithm based on the pessimistic data-driven quantitative feature multi-granularity model to extract fault diagnosis decision rules;

[0034] Step 4: Construct a naive Bayesian classifier model accor...

specific Embodiment approach 2

[0035] Embodiment 2: The difference between this embodiment and Embodiment 1 is that the feature similarity between instances in the step 2 is defined as follows:

[0036] For an incomplete information system S=(U,A=C∪D,V,f), U is the instance set, A is the attribute set, C is the symptom attribute set, D is the decision attribute set, V is the value range of A, f is a mapping function, let the attribute set right in Indicates all the different known attribute values ​​of the instance on the attribute b, Indicates that the attribute value of the instance on the attribute b is b i the number of instances of , then Feature similarity VR on attribute set B B (x,y) calculation formula is:

[0037] VR B (x,y)=Π b∈B R b (x,y)·N B (x,y) (1)

[0038] Among them, N B (x, y) represents the proportion of missing attribute values ​​in instance x and y, R b (x, y) represents the feature similarity between instances x and y on attribute b, |X| represents the cardinality ...

specific Embodiment approach 3

[0044]Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that the definition of data-driven quantitative feature relationship in Step 2 is as follows:

[0045] For an incomplete information system S=(U,A=C∪D,V,f), U is the instance set, A is the attribute set, C is the symptom attribute set, D is the decision attribute set, V is the value range of A, f is a mapping function, let the attribute set Then the data-driven quantitative feature relationship is:

[0046] VR(B)={(x,y)∈U×U|y∈K B (x), VR B (x,y)≥α} (4)

[0047] Among them, α is the threshold. If the threshold α is too large, the feature set that satisfies the data-driven quantitative feature relationship contains too few instances; if the threshold α is too small, the feature set that satisfies the data-driven quantitative feature relationship contains too many instances. In order to avoid the occurrence of the above situation, the average value of the minimum feature similarity between a...

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 fault diagnosis method for a planetary gear box based on data-driven quantitative characteristic multi-granularities and aims to solve the problem of fault diagnosis of the planetary gear box under incomplete information. A specific process of the method comprises the steps of I, extracting fault diagnosis characteristics according to a collected typical fault planetary gear box characteristic signal, and establishing an incomplete fault diagnosis information system; II, analyzing the incomplete fault diagnosis information system by use of a data-driven quantitative characteristic relation, calculating a characteristic similarity between all examples to obtain a characteristic set which satisfies the data-driven quantitative characteristic relation; III, extractinga fault diagnosis decision-making rule by use of an attribute reduction algorithm based on a pessimistic data-driven quantitative characteristic multi-granularity model; and IV, constructing a naiveBayes classifier model according to the fault diagnosis decision-making rule to deduce the state of a to-be-diagnosed planetary gear box. Results of the embodiment show that an indistinguishable relation between the examples can be accurately judged and the accurate rate of fault diagnosis is improved.

Description

technical field [0001] The invention relates to a fault diagnosis method, in particular to a multi-granularity planetary gearbox fault diagnosis method based on data-driven quantitative features. Background technique [0002] Planetary gearboxes have been widely used in mechanical transmission systems of equipment such as helicopters, wind turbines or transportation vehicles. It has the characteristics of small size, compact structure, high precision, large transmission ratio and strong bearing capacity. However, due to long-term operation in complex and harsh environments such as high-speed and heavy loads, key parts such as sun gears, planetary gears, planetary carriers, and ring gears in planetary gearboxes are prone to failures such as cracks or pitting; thus inducing equipment failures and causing Huge economic losses, and even serious consequences such as operator casualties. Therefore, in order to ensure the normal operation of the equipment, reduce the maintenance ...

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): G06K9/62
CPCG06F18/22G06F18/24155
Inventor 于军
Owner HARBIN UNIV OF SCI & TECH
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