Structural damage detection using principal component analysis of frequency response function data

2020 ◽  
Vol 27 (7) ◽  
Author(s):  
Akbar Esfandiari ◽  
Mansureh‐Sadat Nabiyan ◽  
Fayaz R. Rofooei
Keyword(s):  
Principal Component Analysis ◽  
Damage Detection ◽  
Frequency Response ◽  
Response Function ◽  
Structural Damage ◽  
Frequency Response Function ◽  
Principal Component ◽  
Component Analysis ◽  
Structural Damage Detection ◽  
Function Data

scholarly journals A Novel Method for Fault Diagnosis of the Two-Input Two-Output Nonlinear Mass-Spring-Damper System Based on NOFRF and MBPCA

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jialiang Zhang ◽  
Jie Wu ◽  
Xiaoqian Zhang
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Nonlinear System ◽  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Principal Component ◽  
Component Analysis ◽  
Novel Method ◽  
Mass Spring

For fault diagnosis of the two-input two-output mass-spring-damper system, a novel method based on the nonlinear output frequency response function (NOFRF) and multiblock principal component analysis (MBPCA) is proposed. The NOFRF is the extension of the frequency response function of the linear system to the nonlinear system, which can reflect the inherent characteristics of the nonlinear system. Therefore, the NOFRF is used to obtain the original fault feature data. In order to reduce the amount of feature data, a multiblock principal component analysis method is used for fault feature extraction. The least squares support vector machine (LSSVM) is used to construct a multifault classifier. A simplified LSSVM model is adopted to improve the training speed, and the conjugate gradient algorithm is used to reduce the required storage of LSSVM training. A fault diagnosis simulation experiment of a two-input two-output mass-spring-damper system is carried out. The results show that the proposed method has good diagnosis performance, and the training speed of the simplified LSSVM model is significantly higher than the traditional LSSVM.

Structural damage detection in plates using a deep neural network–couple sparse coding classification ensemble method

2020 ◽  
pp. 107754632092915
Author(s):  
Vahid Bokaeian ◽  
Faramarz Khoshnoudian ◽  
Milad Fallahian
Keyword(s):  
Damage Detection ◽  
Frequency Response ◽  
Response Function ◽  
Sparse Coding ◽  
Structural Damage ◽  
Frequency Response Function ◽  
Principal Component ◽  
Plate Structures ◽  
Feature Extraction Method ◽  
Damage Scenarios

The present study aims at identifying damages in plate structures by applying a pattern recognition–based damage detection technique using the frequency response function. The large number of degrees of freedom is one of the crucial obstacles in the way of accurately identifying damages in plate structures. On the other hand, frequency response functions include many details that dramatically lower the computing speed and enlarge the memory needed for storing data, hampering the application of this method. Furthermore, this study performs principal component analysis as an authoritative feature extraction method with the purpose of reducing the dimensions of the measured frequency response function data and generating distinct feature patterns. Also, because there has been no individual optimal classifier applicable to all problems, an ensemble comprising two powerful classifiers containing couple sparse coding classification and deep neural networks is used to predict the structure damage. This study evaluates the accuracy of damage detection by the proposed method in square-shaped structural plates with the lengths of 1 m and 2 m under different damage scenarios, namely, single and multiple element.

Sign in / Sign up

Export Citation Format

Share Document