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

Online identification method for time-varying working mode of eigenvector recursion with forgetting factor

A technology of eigenvectors and forgetting factors, which is applied in the field of principal component analysis linear time-varying structural work modal parameter identification, can solve the problem of not being able to identify modal parameters online

Active Publication Date: 2017-02-22
HUAQIAO UNIVERSITY
View PDF6 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The working mode recognition methods based on PCA, ICA, SOBI and manifold learning are all statistical mode recognition methods, which are only suitable for linear time-invariant structures, and cannot identify the modal parameters of linear time-varying structures online.

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
  • Online identification method for time-varying working mode of eigenvector recursion with forgetting factor
  • Online identification method for time-varying working mode of eigenvector recursion with forgetting factor
  • Online identification method for time-varying working mode of eigenvector recursion with forgetting factor

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0140] Such as image 3 As shown, it is a three-degree-of-freedom spring oscillator system that simulates environmental excitation. The system is a slow time-varying system with weak damping. The mass m of block 1 1 is time-varying, m 2 、m 3 The mass remains constant to simulate a time-varying mass system; the external excitation uses Gaussian white noise with a mean value of 0 and a variance of 1 (in many practical problems, external environments that are difficult to measure are often simulated with white noise to solve problems) .

[0141] In this embodiment, the method for identifying working modal parameters of a linear time-varying structure based on eigenvalue eigenvector recursion with forgetting factor recursive principal component analysis uses a three-degree-of-freedom spring oscillator to simulate a time-varying structure, wherein, m 2 =1kg, m 3 = 1 kg; k 1 =1000N / m,k 2 =1000N / m,k 3 =1000N / m; c 1 =0.01N.s / m,c 2 =0.01N.s / m, c 3 =0.01 N.s / m. The initial ...

Embodiment 2

[0186] Such as Figure 32 As shown, it is a finite element model that discretizes the cantilever beam into 40 elements;

[0187] In this embodiment, the method of principal component analysis linear time-varying structure working modal parameter identification method using eigenvalue eigenvector recursion with forgetting factor uses density-time-varying cantilever beams to simulate time-varying structures, wherein the beam parameter setting As follows: the size is 1×0.02×0.02m 3 (length × width × height), the cross-sectional area is A=W×H=4×10 -4 m 2 , the moment of inertia is I=WH 3 / 12, Young's modulus E=2.1×10 11 N / m 2 , Poisson's ratio u=0.3, the density is

[0188] Figure 33 The identification frequency for the proposed method, the sliding window recursive principal component analysis algorithm to identify the first-order natural frequency and the theoretical first-order natural frequency comparison;

[0189] Figure 34 The identification frequency for the pro...

Embodiment 3

[0234] Figure 55 is the block diagram of the working mode parameter device design system;

[0235] Figure 56 is the design block diagram of the upper computer;

[0236] Such as Figure 55 Shown, the time-varying structure operating mode parameter identification device based on the recursive principal component analysis algorithm of band forgetting factor of the present invention, comprises by OMAP processor (possess dual-core structure, ARM core+DSP core, has low power consumption , strong data processing capability) composed of control and data processing modules, give full play to the ability of DSP signal processing and ARM control; vibration data acquisition module (including signal input, signal conditioning, A / D data acquisition and conversion, etc. ); storage module (store a large amount of vibration data); liquid crystal display module (use LCD liquid crystal screen as output to display diagnostic results and waveform information); power supply module (responsible...

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 an online identification method for working mode parameters of a linear time-varying structure of principal component analysis of eigenvalue eigenvector recursion with a forgetting factor. According to the method, thoughts of ''the forgetting factor'', ''online recursion'', ''eigenvalue eigenvector recursion'' and ''matrix rank-1 correction'' are introduced based on identification of working mode parameters of a linear time-invariant structure of classic principal component analysis, so that a time-varying transient modal vibration shape and an inherent frequency of the linear time-varying structure can be identified only from a non-stationary vibration response signal. An eigenvalue eigenvector is directly subjected to online recursion updating, so that the shortcoming that a principal component model needs to be repeatedly updated in a conventional method for identifying time-varying working mode parameters of recursive principal component analysis is overcome, the algorithm time and the space complexity are shortened and lowered, online real-time time-varying working mode parameter identification is realized, and dynamic change characteristics of the working mode parameters of the structure can be effectively monitored; and the method can be used for equipment fault diagnosis, health monitoring and system structure analysis and optimization.

Description

technical field [0001] The invention relates to the field of modal parameter identification, in particular to a principal component analysis linear time-varying structural working modal parameter identification method with eigenvalue eigenvector recursion with forgetting factor. Background technique [0002] With the development and progress of science and technology, engineering structures in the fields of aerospace, architecture, bridges, oceans, machinery, etc. are gradually developing in the direction of large-scale, complex, and intelligent. The loads on the structures are difficult to measure. To establish their dynamics Model, the traditional method of obtaining system modal parameters based on measurement input and output is difficult to apply, and only the working modal parameter identification method that only needs to measure the output response can be used. Moreover, in the actual operation of many engineering structures, due to various internal and external exci...

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): G06F19/00
CPCG16Z99/00
Inventor 王成官威陈锻生张天舒陈叶旺王建英王田张惠臻
Owner HUAQIAO UNIVERSITY
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