Equipment health state evaluation and recession prediction method based on multi-channel sensing signals

A technology for sensing signals and health status, which is applied in the field of modern equipment status monitoring and health pre-diagnosis and maintenance. Engineering applicability, improving equipment production efficiency, and reducing dimensions

Inactive Publication Date: 2012-01-11
SHANGHAI UNIV
View PDF1 Cites 36 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The system has the following deficiencies: [1] It is useless to perform multi-sensing signal joint noise filtering processing on the collected multi-sensing signal, which may cause the signal to contain more noise; [2] The principal component analysis method is only used in the feature extraction stage. The feature of the maximum global variance informat

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
  • Equipment health state evaluation and recession prediction method based on multi-channel sensing signals
  • Equipment health state evaluation and recession prediction method based on multi-channel sensing signals

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0015] Embodiment one: if figure 1 As shown, the equipment health status assessment and prediction method based on multiple sensor signals, the specific steps are as follows:

[0016] 1. Arrange sensors at key positions on the equipment or components to pick up various signals (such as vibration, displacement, current, voltage, pressure, etc.) that can reflect the health status of the equipment. For data acquisition, the data acquisition card also converts the analog signal into a digital signal. The multi-sensing signal joint noise filtering algorithm uses the multi-scale wavelet decomposition method to perform the multi-sensing signal joint noise filtering operation. The multi-scale wavelet decomposition method first uses the wavelet decomposition algorithm (using Mallet wavelet) to decompose each sensor signal, and at the same time uses the principal component analysis method to perform principal component analysis on the wavelet coefficients after wavelet decomposition, de...

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 an equipment health state evaluation and recession prediction method based on multi-channel sensing signals. The operation steps of the method are as follows: the first step: joint noise filtration of the multiple sensing signals: adopting a multi-scale wavelet filtering algorithm to perform the joint noise filtration on the multi-channel sensing signal collected from equipment so as to improve the quality of the multiple sensing signals; the second step: generation of an original characteristic set; the third step: extraction of local characteristics of the signals; the fourth step: modeling of a reference self-organizing mapping model; and the fifth step: health quantitative evaluation and residual life prediction. By adopting the method, the quantitative evaluation and the life prediction of performance recession of the equipment can be realized, the operation reliability of the equipment can be further improved and the maintenance cost can be reduced.

Description

technical field [0001] The invention is a method for evaluating and predicting the health state of equipment based on multiple sensing signals, involving joint noise filtering of multiple sensing signals, generation of original feature sets, local feature extraction, benchmark self-organizing mapping model modeling, health quantitative assessment and Remaining life prediction, realizing real-time quantitative evaluation of equipment health status and prediction of decline trend. The invention belongs to the technical field of modern equipment state monitoring and health prediagnosis and maintenance. Background technique [0002] At present, key equipment presents a development trend of high automation, high precision, high reliability and high intelligence, emphasizing the controllability, reliability and maintainability of equipment. The sudden occurrence of equipment failure will not only greatly increase the maintenance cost of the enterprise, but also seriously affect t...

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): G01D21/02G06F19/00
Inventor 余建波刘美芳
Owner SHANGHAI UNIV
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