A multivariate-based equipment dynamic health state assessment method

A health status and multi-variable technology, applied to instruments, character and pattern recognition, computer components, etc., can solve the problems of evaluation result deviation, lack of comprehensive knowledge of equipment failure mode mechanism, etc., to reduce training time and achieve accurate health status Evaluate the effect on lifetime prediction, improve accuracy and generalization ability

Active Publication Date: 2019-05-03
GUANGDONG UNIV OF TECH
View PDF10 Cites 11 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The technical effect that this invention focuses on evaluating equipment's performance based upon their current environmental status during operation. It suggests assigning specific levels or ranges of values called critical points at certain times when there are no significant problems with them. These critical points help predict how well these devices will last over its lifetime without being damaged by any factors like vibration or impact from external influences such as rainwater. By doing so, they improve maintenance efficiency while reducing costs associated with replacing worn out components.

Problems solved by technology

Technological Problem: Current solutions involve manual inspections and fail predictions made after significant periods of non-operational downgrades over their lifespan. These techniques can be expensive and prone to errors caused by subjectivity and poor judgment. Additionally, these approaches only provide static estimates without accounting for future changes such as deteriorating components undergoing agitation within an operational period.

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
  • A multivariate-based equipment dynamic health state assessment method
  • A multivariate-based equipment dynamic health state assessment method
  • A multivariate-based equipment dynamic health state assessment method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0047] The present invention will be further described below in conjunction with specific embodiment:

[0048] A multivariate-based equipment dynamic health status assessment method described in this embodiment uses a multivariate model combined with SOCNN (Significance-Offse Convolutional Neural Network) and S-MEFC (Subtractive MaximumEntropy Fuzzy Clustering) to perform equipment dynamic health status assessment , the model structure is as figure 1 shown.

[0049] The model is divided into two parts: offline and online:

[0050] The offline part includes: the construction of SOCNN multivariate prediction model and classifier. Given a training dataset L with multi-dimensional features. According to the dimensionality of the feature space, the SOCNN algorithm is used to establish the multivariate predictor Pi, and the data of m cases are used for training. Subsequently, the S-MEFC algorithm is used to construct q unsupervised classifiers for the multidimensional feature tr...

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 multivariable-based equipment dynamic health state assessment method. A multivariable model combining the comprehensive significance-bias convolutional neural network and thedecreasing maximum entropy fuzzy clustering is utilized to perform equipment dynamic health state evaluation; the method has the advantages that the training time is shortened, the model precision and generalization ability are improved, the fault threshold can be dynamically distributed according to the environment, the real working condition of the equipment is met, and the accurate health state evaluation and life prediction of the equipment are realized.

Description

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

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
Owner GUANGDONG UNIV OF TECH
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

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap