RE-EXAMINATION OF THICKNESS-RESONANCE- FREQUENCY FORMULA FOR STRUCTURAL INTEGRITY APPRAISAL AND DAMAGE DIAGNOSIS

2012 ◽  
Vol 04 (01n02) ◽  
pp. 1250011
Author(s):  
RUICHONG ZHANG
Keyword(s):  
Data Analysis ◽  
Resonant Frequency ◽  
Correction Factor ◽  
Structural Integrity ◽  
Data Sets ◽  
Damage Diagnosis ◽  
Thickness Estimation ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Shape Correction

This study examines rationale of correction factor β in the formula of thickness resonant frequency, fundamental to the thickness estimation of impact-echo (IE) approach in nondestructive testing (NDT) for integrity appraisal and damage diagnosis of infrastructure systems. It shows the role of the factor in the formula from the perspective of testing equipment setup, wave propagation, and resonant frequency identification, much broader than what was first introduced empirically for shape correction of a structure under test. Emphasis is laid in wave-based interpretation of resonant frequency, typically obtained from traditional fast Fourier transform (FFT) data analysis of IE recordings. Since the FFT data analysis provides average, not true, characteristic of resonant frequency shown in the nonstationary IE recordings, it typically distorts the thickness estimation from the formula if the correction factor is not used. An adaptive time-frequency data analysis termed Hilbert–Huang transform (HHT) is then introduced to overcome the shortage of FFT analysis in identifying the resonant frequency from noise-added IE recordings. With FFT and HHT analyses of five data sets of sample IE recordings from sound and damaged concrete structures and comparison with referenced ones, this study reveals that the proposed IE approach with HHT data analysis not only eliminates the subjective use of correction factor in the formula, but it also improves greatly the accuracy in the thickness estimation.

DTI data analysis: application of fiber tracking to group averaged data sets

2010 ◽  
Vol 41 (01) ◽  
Author(s):  
HP Müller ◽  
A Unrath ◽  
A Riecker ◽  
AC Ludolph ◽  
J Kassubek
Keyword(s):  
Data Analysis ◽  
Fiber Tracking ◽  
Data Sets ◽  
Analysis Application

Characterization of Cavitation During Pump Operation Based on Hilbert-Huang Transform

2018 ◽  
Author(s):  
Hui Sun ◽  
Shouqi Yuan ◽  
Yin Luo ◽  
Bo Gong
Keyword(s):  
Centrifugal Pump ◽  
Longitudinal Vibration ◽  
Process Time ◽  
Current Signal ◽  
Vibration Signals ◽  
Pump Operation ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Cavitation Process ◽  
Non Stationary Signal

Cavitation has negative influence on pump operation. In order to detect incipient cavitation effectively, experimental investigation was conducted to through acquisition of current and vibration signals during cavitation process. In this research, a centrifugal pump was modeled for research. The data was analyzed by HHT method. The results show that Torque oscillation resulted from unsteady flow during cavitation process could result in energy variation. Variation regulation of RMS of IMF in current signal is similar to that in axial vibration signal. But RMS of IMF in current signal is more sensitive to cavitation generation. It could be regarded as the indicator of incipient cavitation. RMS variation of IMF in base, radial, longitudinal vibration signals experiences an obvious increasing when cavitation gets severe. Such single variation regulation could be selected as the indicator of cavitation stage recognition. Hilbert-Huang transform is suitable for transient and non-stationary signal process. Time-frequency characteristics could be extracted from results of HHT process to reveal pump operation condition. The contents of current work could provide valuable references for further research on centrifugal pump operation detection.

scholarly journals A multilevel approach to big data analysis using analytic tools and actor network theory

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Tiko Iyamu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Network Theory ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Actor Network Theory ◽  
Big Data Analysis ◽  
Data Sets ◽  
Multilevel Approach ◽  
Actor Network

Background: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that they do. This has a negative impact on the accuracy, redundancy and usefulness of data sets, which in turn affects the value of operations and the competitive effectiveness of an organisation. Also, the current single approach lacks a detailed examination of data sets, which big data deserve in order to improve purposefulness and usefulness.Objective: The purpose of this study was to propose a multilevel approach to big data analysis. This includes examining how a sociotechnical theory, the actor network theory (ANT), can be complementarily used with analytic tools for big data analysis.Method: In the study, the qualitative methods were employed from the interpretivist approach perspective.Results: From the findings, a framework that offers big data analytics at two levels, micro- (strategic) and macro- (operational) levels, was developed. Based on the framework, a model was developed, which can be used to guide the analysis of heterogeneous data sets that exist within networks.Conclusion: The multilevel approach ensures a fully detailed analysis, which is intended to increase accuracy, reduce redundancy and put the manipulation and manifestation of data sets into perspectives for improved organisations’ competitiveness.

Vibration Fatigue Test Based on Fiber Bragg Grating Sensors and HHT

2013 ◽  
Vol 328 ◽  
pp. 193-197
Author(s):  
Si Jin Xin ◽  
Zhen Tong
Keyword(s):  
Titanium Alloy ◽  
Fiber Bragg Grating ◽  
Fatigue Test ◽  
Alloy Sheet ◽  
Bragg Grating ◽  
Metal Fatigue ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Vibration Fatigue ◽  
Fbg Sensors

The metal fatigue is an important factor to cause an accident in machine operation, so metal fatigue test is a significant procedure in manufacturing. Fiber Bragg Grating (FBG), as an innovative sensor, has been applied to the measurement of various rotating machines. In this paper, the time-frequency analysis is used to detect the fatigue feature of a titanium alloy measured by FBG sensors. Furthermore, the Hilbert-Huang transform (HHT) is more effective to observe the fatigue limit of the titanium alloy sheet, compared to the Wavelet transform (WT).

scholarly journals Common Crossing Structural Health Analysis with Track-Side Monitoring

2019 ◽  
Vol 21 (3) ◽  
pp. 77-84
Author(s):  
Mykola Sysyn ◽  
Olga Nabochenko ◽  
Franziska Kluge ◽  
Vitalii Kovalchuk ◽  
Andriy Pentsak
Keyword(s):  
Gaussian Process Regression ◽  
Ensemble Empirical Mode Decomposition ◽  
Frequency Resolution ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Structural Health ◽  
Rolling Surface ◽  
Mode Decomposition ◽  
Inertial Measurements ◽  
Common Crossing

Track-side inertial measurements on common crossings are the object of the present study. The paper deals with the problem of measurement's interpretation for the estimation of the crossing structural health. The problem is manifested by the weak relation of measured acceleration components and impact lateral distribution to the lifecycle of common crossing rolling surface. The popular signal processing and machine learning methods are explored to solve the problem. The Hilbert-Huang Transform (HHT) method is used to extract the time-frequency features of acceleration components. The method is based on Ensemble Empirical Mode Decomposition (EEMD) that is advantageous to the conventional spectral analysis methods with higher frequency resolution and managing nonstationary nonlinear signals. Linear regression and Gaussian Process Regression are used to fuse the extracted features in one structural health (SH) indicator and study its relation to the crossing lifetime. The results have shown the significant relation of the derived with GPR indicator to the lifetime.

scholarly journals ReactomeFIViz: the Reactome FI Cytoscape app for pathway and network-based data analysis

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 146 ◽  
Author(s):  
Guanming Wu ◽  
Eric Dawson ◽  
Adrian Duong ◽  
Robin Haw ◽  
Lincoln Stein
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Graphical Models ◽  
High Throughput ◽  
Interaction Network ◽  
Large Data ◽  
Relevant Information ◽  
Data Sets ◽  
Data Types ◽  
Biological Studies

High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network and human curated pathways from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.

scholarly journals Structural Damage Diagnosis and Prediction Using Machine Learning and Deep Learning Models: Comprehensive Review of Advances

2019 ◽  
Author(s):  
Amir Mosavi
Keyword(s):  
Machine Learning ◽  
Composite Structures ◽  
Structural Damage ◽  
Structural Integrity ◽  
State Of The Art ◽  
Future Trends ◽  
Learning Models ◽  
Damage Diagnosis ◽  
Comprehensive Review ◽  
Initiation And Propagation

The loss of integrity and adverse effect on mechanical properties can be concluded as attributing miro/macro-mechanics damage in structures, especially in composite structures. Damage as a progressive degradation of material continuity in engineering predictions for any aspects of initiation and propagation requires to be identified by a trustworthy mechanism to guarantee the safety of structures. Besides the materials design, structural integrity and health are usually prone to be monitored clearly. One of the most powerful methods for the detection of damage is machine learning (ML). This paper presents the state of the art of ML methods and their applications in structural damage and prediction. Popular ML methods are identified and the performance and future trends are discussed.

Identification of Fault Signals in Rotating Machinery Using Higher Order Time Frequency Analysis

1997 ◽  
Author(s):  
Sang-Kwon Lee ◽  
Paul R. White
Keyword(s):  
Fault Diagnosis ◽  
Wigner Distribution ◽  
Higher Order ◽  
Rotating Machinery ◽  
Data Sets ◽  
Higher Order Statistics ◽  
Vibration Signals ◽  
Time Frequency ◽  
Increasing Trend ◽  
Monitoring Application

Abstract Impulsive acoustic and vibration signals within rotating machinery are often induced by irregular impacting. Thus the detection of these impulses can be useful for fault diagnosis. Recently there is an increasing trend towards the use of higher order statistics for fault detection within mechanical systems based on the observation that impulsive signals tend to increase the kurtosis values. We show that the fourth order Wigner Moment Spectrum, called the Wigner Trispectrum, has superior detection performance to second order Wigner distribution for typical impulsive signals found in a condition monitoring application. These methods are also applied to data sets measured within a car engine and industrial gearbox.

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