scholarly journals The Researches on Damage Detection Method for Truss Structures

2018 ◽  
Vol 38 ◽  
pp. 03030
Author(s):  
Meng Hong Wang ◽  
Xiao Nan Cao
Keyword(s):  
Numerical Simulation ◽  
Frequency Response ◽  
Response Function ◽  
Time Domain ◽  
Experimental Analysis ◽  
Frequency Response Function ◽  
Principal Component ◽  
Truss Structures ◽  
Time Domain Signal ◽  
The Time Domain

This paper presents an effective method to detect damage in truss structures. Numerical simulation and experimental analysis were carried out on a damaged truss structure under instantaneous excitation. The ideal excitation point and appropriate hammering method were determined to extract time domain signals under two working conditions. The frequency response function and principal component analysis were used for data processing, and the angle between the frequency response function vectors was selected as a damage index to ascertain the location of a damaged bar in the truss structure. In the numerical simulation, the time domain signal of all nodes was extracted to determine the location of the damaged bar. In the experimental analysis, the time domain signal of a portion of the nodes was extracted on the basis of an optimal sensor placement method based on the node strain energy coefficient. The results of the numerical simulation and experimental analysis showed that the damage detection method based on the frequency response function and principal component analysis could locate the damaged bar accurately.

Characterization of Electro-Active Paper Vibration Sensor by Impact Testing and Random Excitation

2015 ◽  
Vol 07 (04) ◽  
pp. 1550065 ◽  
Author(s):  
Zafar Abas ◽  
Dong Ho Yang ◽  
Heung Soo Kim ◽  
Moon Kyu Kwak ◽  
Jaehwan Kim
Keyword(s):  
Frequency Response ◽  
Response Function ◽  
Polyvinylidene Fluoride ◽  
Frequency Response Function ◽  
Low Frequency ◽  
Random Excitation ◽  
Impact Testing ◽  
Vibration Sensor ◽  
Dynamic Strain ◽  
The Time Domain

We characterized a vibration sensor made of piezoelectric paper by measuring the frequency response function of an aluminum cantilever that was subjected to impulse loading and random excitation. The dynamic characteristics of the device were measured by recording the transient response of the smart cantilever beam with a pair of electro-active paper (EAPap) and polyvinylidene fluoride (PVDF) sensors located at a 5 mm distance from the clamped end as well as from a second pair of piezoelectric sensors located at a distance of 140 mm. The responses were measured by impacting the cantilever at its tip and at its mid-point. A fast Fourier transform was applied on the time domain data to measure the resonant frequencies of the vibrating structure. Both the EAPap and the PVDF sensors were observed to be very sensitive to varying levels of dynamic strain. The EAPap sensor showed a low strain sensitivity that was found to be desirable due to the inherent piezoelectricity and eco-friendly behavior of the material. The results revealed that the dynamic sensing ability of the EAPap at a low frequency range was quite comparable to that of PVDF when monitoring structural vibrations. The frequency response function was also measured via random excitation, piezoelectricity of the EAPap sensor shows potential for sensing vibrations with a dynamic response.

Rolling Bearing Fault Diagnosis Research

2012 ◽  
Vol 155-156 ◽  
pp. 87-91
Author(s):  
Zhong Hu Yuan ◽  
Yang Su ◽  
Xiao Xuan Qi
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Recognition Rate ◽  
Principal Component ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Time Domain Signal ◽  
Feature Information ◽  
The Time Domain

According to the characteristics of the rolling bearing fault, we make the research on fault diagnosis. Time domain signal can not perform the fault feature information well. The power spectrum changes the time domain signals into the frequency signals. It sets up the new data model. It uses the principal component analysis on fault diagnosis. It uses T square statistics and Q statistics methods to make fault diagnosis. Simulation experiment results demonstrate that this method provides a high recognition rate.

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.

Investigation and Modification of Frequency Response Function Using Digital Fourier Analysis for High Speed Dynamic Testing Facility

2009 ◽  
Author(s):  
Chandrashekhar K. Thorbole ◽  
Keshavanarayana S. Raju
Keyword(s):  
Frequency Response ◽  
Frequency Domain ◽  
Response Function ◽  
Time Domain ◽  
Frequency Response Function ◽  
Detection System ◽  
Structural Response ◽  
Wave Form ◽  
Data Set ◽  
Load Detection

The increasing application of composites in the aviation and automobile industry demands a better understanding of composite material behavior under high loading rate. This shall provide a better insight of actual loads on occupants while preserving livable crashworthy structure. In this study, a high stroke rate MTS servo-hydraulic testing machine is used to characterize the behavior of composite materials at high strain rates. At higher stroke rates, the output of the load detection system acquired by the load cell deviates from the true load-time wave form of the specimen. This is due to the convolution of the structural response of the detection system with the true characteristic of the specimen. To identify the true nature of the specimen load-time behavior, the de-convolution of the detection system response is necessary to restore the specimen characteristic wave form closer to its true behavior. The convolution of data set in the time domain is a time consuming process which explains the benefit of using the frequency domain; as the convolution in time domain corresponds to multiplication in the frequency domain. This process requires the transformation of the time domain data to frequency domain data via Fast Fourier Transform (FFT). In the frequency domain the complex division of the Fourier transfer of the detection system output with frequency response function of the detection system shall provide the true complex input characteristic. This paper elaborates the methodology utilized for obtaining the Frequency Response Function (FRF) of the load detection system using digital Fourier analysis with a single input/output data set. This also emphasizes precautions and guidelines for improving results with FFT to obtain true FRF measurements of the load detection system. The FRF obtained is successfully used to identify the actual specimen wave form characteristic. This is achieved by extracting the structural response of the load detection system from the load cell output.

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.

scholarly journals Identification of damage-expected members of truss structures using frequency response function

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668791 ◽  
Author(s):  
Moon-Jeong Kim ◽  
Hee-Chang Eun
Keyword(s):  
Damage Detection ◽  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Truss Structure ◽  
Truss Structures ◽  
Modal Data ◽  
Damage Indices ◽  
Proper Orthogonal Modes ◽  
Proper Orthogonal

A damaged member in a truss structure leads to a variation in the initial responses of its adjacent members. A flexibility-based approach extracting from the modal data should be implemented as one of the structural damage detection methods. The frequency response function data as dynamic measurements provide more information on the system characteristics compared with modal data. Proper orthogonal modes from the frequency response functions extracted in the given frequency ranges and their modified forms can be utilized as damage indices to detect damages. This study considers damage detection of a truss structure using a frequency response function–based approach transformed to the proper orthogonal modes and a flexibility-based approach using the first few modal data for undamaged and damaged states. The utilization of these two methods is compared through numerical experiments on truss structures. The methods can rarely detect the damaged member accurately, but a group of damage-expected members is detected despite the existence of external noise. It is shown that the frequency response function–based approach can be utilized more explicitly than the flexibility-based approach.

scholarly journals Uncertainties Consideration in Empirical Frequency Response Function Data for Damage Identification Based On Artificial Neural Network

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Khairul H. Padil ◽  
◽  
Norhisham Bakhary ◽  
Wan Nur Firdaus Wan Hassan ◽  
Nadirah Darus ◽  
...  
Keyword(s):  
Frequency Response ◽  
Response Function ◽  
Structural Damage ◽  
Frequency Response Function ◽  
Damage Identification ◽  
Principal Component ◽  
Steel Truss Bridge ◽  
Artificial Neural ◽  
Actual Damage ◽  
Truss Bridge

The modern application of frequency response function (FRF) with artificial neural networks (ANN) has become one of the leading methods in vibration-based damage detection approach. However, since full-size empirically obtained FRF data is used as ANN input, a broad composition ANN input layer series would occur. Consequently, principal component analysis (PCA) is adopted to compress the FRF data magnitude. Despite this, PCA alone is unable to select the important FRF data features effectively, due to the exceedingly FRF data size in addition with existing uncertainties. Therefore, this study proposed the merger of a non-probabilistic analysis and ANN approach with PCA by considering the uncertainties effect and the inefficiency of using empirical FRF data. The empirical FRF data is obtained from a steel truss bridge structure. The results show that the PoDE values above 95% are measured at the particular executed damage locations and the DMI values show the damage severity at the actual damage locations. Overall, the results show that the proposed method is capable in considering the uncertainties effect on the empirical FRF data for structural damage identification.

Sign in / Sign up

Export Citation Format

Share Document