Study on Failure Mechanisms of Composite Materials Based on HHT

2013 ◽  
Vol 477-478 ◽  
pp. 30-33
Author(s):  
Wen Qin Han ◽  
Jin Yu Zhou
Keyword(s):  
Composite Materials ◽  
Failure Mechanisms ◽  
Acoustic Emissions ◽  
Deep Understanding ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Composite Failure ◽  
Mode Decomposition ◽  
Materials Failure ◽  
Twill Weave

In order to get a deep understanding of composite failure mechanisms, the new advanced signal processing methodologies are established for the analysis of acoustic emission (AE) data obtained from the quasi-static tension test of composite materials. Tensile test were carried out on twill-weave composite specimens, and acoustic emissions were recorded from these tests. AE signals were decomposed into a set of Intrinsic Mode Functions (IMF) components by means of Empirical Mode Decomposition (EMD) , the Hilbert-Huang Transform (HHT) of each IMF component was performed, it was shown that the frequency distribution of IMF component in time-scale could be directly related to composite materials failure mechanisms.

Identification of Damage Modes in Twill-Weave Composite Based on EMD of AE Event

2012 ◽  
Vol 198-199 ◽  
pp. 60-63
Author(s):  
Wen Qin Han ◽  
Jin Yu Zhou
Keyword(s):  
Acoustic Emission ◽  
Fourier Transform ◽  
Composite Materials ◽  
Acoustic Emissions ◽  
Peak Frequency ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Different Types ◽  
Twill Weave ◽  
Ae Signals

Acoustic emission (AE) monitoring is the primary technology used for the identification of different types of failure in composite materials. Tensile test were carried out on twill-weave composite specimens, and acoustic emissions were recorded from these tests. AE signals were decomposed into a set of Intrinsic Mode Functions(IMF) components by means of Empirical Mode Decomposition(EMD) , the Fast Fourier Transform (FFT) of each IMF component was performed, it was shown that the event peak frequency of each IMF component could be directly related to the materials damage modes.

scholarly journals Physical modelling of failure in composites

2016 ◽  
Vol 374 (2071) ◽  
pp. 20150280 ◽  
Author(s):  
Ramesh Talreja
Keyword(s):  
Composite Materials ◽  
Failure Modes ◽  
Structural Integrity ◽  
Failure Mechanisms ◽  
Local Stress ◽  
Physical Modelling ◽  
Stress Fields ◽  
Systematic Analysis ◽  
Composite Failure ◽  
Failure Theories

Structural integrity of composite materials is governed by failure mechanisms that initiate at the scale of the microstructure. The local stress fields evolve with the progression of the failure mechanisms. Within the full span from initiation to criticality of the failure mechanisms, the governing length scales in a fibre-reinforced composite change from the fibre size to the characteristic fibre-architecture sizes, and eventually to a structural size, depending on the composite configuration and structural geometry as well as the imposed loading environment. Thus, a physical modelling of failure in composites must necessarily be of multi-scale nature, although not always with the same hierarchy for each failure mode. With this background, the paper examines the currently available main composite failure theories to assess their ability to capture the essential features of failure. A case is made for an alternative in the form of physical modelling and its skeleton is constructed based on physical observations and systematic analysis of the basic failure modes and associated stress fields and energy balances. This article is part of the themed issue ‘Multiscale modelling of the structural integrity of composite materials’.

scholarly journals Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition

2014 ◽  
Vol 31 (9) ◽  
pp. 1982-1994 ◽  
Author(s):  
Xiaoying Chen ◽  
Aiguo Song ◽  
Jianqing Li ◽  
Yimin Zhu ◽  
Xuejin Sun ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Feature Extraction Method ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Bidimensional Empirical Mode Decomposition

Abstract It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.

scholarly journals HHT based analysis on seismic response recordings for a base-isolated building

2019 ◽  
Vol 277 ◽  
pp. 02021
Author(s):  
Fei Wang ◽  
Xiandong Kang ◽  
Ting Yan ◽  
Ying Liu
Keyword(s):  
Seismic Response ◽  
Dominant Frequency ◽  
Data Series ◽  
Structural Systems ◽  
Original Signal ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Emd Method

Hilbert-Huang transform (HHT) is proposed to process the seismic response recordings in an 8-story frame-shear wall base-isolated building. Empirical Mode Decomposition (EMD) method is first applied to identify the time variant characteristics and the data series can be decomposed into several components. Hilbert transform is well-behaved in identifying the frequency components. The first 5 intrinsic mode functions (IMFs) are decomposed with their different frequencies. The analytical function is reconstructed and compared with the original signal. They are extremely consistent in amplitude and phase. Based on the IMFs obtained, frequencies of the original signal are inferred at 5 Hz and 1.6 Hz. The higher frequency is regarded as the vibration excited by surface waves. 1.6 Hz is suggested as the dominant frequency of the building. Analysis indicates that HHT is accurate in extracting the dynamic characteristics of structural systems.

WEIGHTED SLIDING EMPIRICAL MODE DECOMPOSITION

2011 ◽  
Vol 03 (04) ◽  
pp. 509-526 ◽  
Author(s):  
R. FALTERMEIER ◽  
A. ZEILER ◽  
A. M. TOMÉ ◽  
A. BRAWANSKI ◽  
E. W. LANG
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Sampling Rate ◽  
Computational Effort ◽  
Intrinsic Mode Functions ◽  
Weighted Version ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Reconstruction Quality ◽  
Spectral Transform

The analysis of nonlinear and nonstationary time series is still a challenge, as most classical time series analysis techniques are restricted to data that is, at least, stationary. Empirical mode decomposition (EMD) in combination with a Hilbert spectral transform, together called Hilbert-Huang transform (HHT), alleviates this problem in a purely data-driven manner. EMD adaptively and locally decomposes such time series into a sum of oscillatory modes, called Intrinsic mode functions (IMF) and a nonstationary component called residuum. In this contribution, we propose an EMD-based method, called Sliding empirical mode decomposition (SEMD), which, with a reasonable computational effort, extends the application area of EMD to a true on-line analysis of time series comprising a huge amount of data if recorded with a high sampling rate. Using nonlinear and nonstationary toy data, we demonstrate the good performance of the proposed algorithm. We also show that the new method extracts component signals that fulfill all criteria of an IMF very well and that it exhibits excellent reconstruction quality. The method itself will be refined further by a weighted version, called weighted sliding empirical mode decomposition (wSEMD), which reduces the computational effort even more while preserving the reconstruction quality.

DETECTION AND QUANTIFICATION OF CONTROL VALVE NONLINEARITIES USING HILBERT–HUANG TRANSFORM

2009 ◽  
Vol 01 (03) ◽  
pp. 425-446 ◽  
Author(s):  
S. BABJI ◽  
P. GORAI ◽  
A. K. TANGIRALA
Keyword(s):  
Power Spectrum ◽  
Fourier Transforms ◽  
Control Valve ◽  
Wave Frequency ◽  
Intrinsic Mode Functions ◽  
Research Activity ◽  
Control Loops ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Valve Stiction

Two of the most important sources of degradation of control loop performance are (i) valve stiction and (ii) tight controller tuning, both of which lead to oscillations in closed–loop outputs. A factor that distinguishes these two sources is the nonlinear signature of the valve stiction; a tightly tuned controller produces oscillations due to a linear source. Detection and isolation of nonlinear fault sources is essential to correctly determine the cause of poor loop performance of control loops. Despite a rich research activity in this area, there is hardly a method which can isolate the simultaneous effects of these two sources. Moreover, the traditional spectral analysis based on Fourier Transforms is largely restricted by the assumption of stationarity in the data to detect and quantify valve nonlinearities. In this work, Hilbert–Huang Transform (HHT) is used to (i) detect valve nonlinearities and (ii) isolate linear and nonlinear fault sources. The key characteristic of HHT is that it represents nonlinearities as intra-wave frequency modulations allowing it to distinguish it from linearities which do not exhibit such modulations. The advantages of HHT-based methods are that (i) nonlinearities translate to a unique signature (ii) nonstationarities in data can be handled in a natural way. It is observed that nonlinearity is captured by a Intrinsic Mode Functions (IMF) obtained from the Empirical Mode Decomposition (EMD) of the process output. The Hilbert–Huang spectrum of these IMFs exhibits intra-wave frequency modulation. The power spectrum of the IMFs shows the presence of harmonics which is used to characterize the valve stiction nonlinearity. Subsequent to detection, quantification is done using the power spectrum of the IMFs. The proposed method is sensitive enough to detect low levels of valve stiction nonlinearities. Results from simulation using one-parameter valve stiction model are presented in support of the proposed methodology. The results demonstrate the advantage and potential of the HHT-based method.

scholarly journals ANALYSIS OF TEMPORAL VARIATIONS IN TURBIDITY FOR A COASTAL AREA USING THE HILBERT-HUANG-TRANSFORM

2011 ◽  
Vol 1 (32) ◽  
pp. 25
Author(s):  
Shigeru Kato ◽  
Magnus Larson ◽  
Takumi Okabe ◽  
Shin-ichi Aoki
Keyword(s):  
Instantaneous Frequency ◽  
River Mouth ◽  
Original Data ◽  
Temporal Variations ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
The Hilbert Transform

Turbidity data obtained by field observations off the Tenryu River mouth were analyzed using the Hilbert-Huang Transform (HHT) in order to investigate the characteristic variations in time and in the frequency domain. The Empirical Mode Decomposition (EMD) decomposed the original data into only eight intrinsic mode functions (IMFs) and a residue in the first step of the HHT. In the second step, the Hilbert transform was applied to the IMFs to calculate the Hilbert spectrum, which is the time-frequency distribution of the instantaneous frequency and energy. The changes in instantaneous frequencies showed correspondence to high turbidity events in the Hilbert spectrum. The investigation of instantaneous frequency variations can be used to understand transitions in the state of the turbidity. The comparison between the Fourier spectrum and the Hilbert spectrum integrated in time showed that the Hilbert spectrum makes it possible to detect and quantify the cycle of locally repeated events.

scholarly journals Data Interpretation Technology of GPR Survey Based on Variational Mode Decomposition

2019 ◽  
Vol 9 (10) ◽  
pp. 2017 ◽  
Author(s):  
Juncai Xu ◽  
Bangjun Lei
Keyword(s):  
Instantaneous Frequency ◽  
Data Interpretation ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Synthetic Signal ◽  
Mode Decomposition ◽  
The Media ◽  
Ground Penetrating

Data interpretation is the crucial scientific component that influences the inspection accuracy of ground penetrating radar (GPR). Developing algorithms for interpreting GPR data is a research focus of increasing interest. The problem of algorithms for interpreting GPR data is unresolved. To this end, this study proposes a sophisticated algorithm for interpreting GPR data with the aim of improving the inspection resolution. The algorithm is formulated by integrating variational mode decomposition (VMD) and Hilbert–Huang transform techniques. With this method, the intrinsic mode function of the GPR data is first produced using the VMD of the data, followed by obtaining the instantaneous frequency by using the Hilbert–Huang transform to analyze the intrinsic mode functions. The instantaneous frequency data can be decomposed into three frequency attributes, including frequency division section, time-frequency section, and space frequency section, which constitute a platform to gain insight into the nature of the GPR data, such that the inspected media components can be examined. The effectiveness of the proposed method on a synthetic signal from a GPR forward model was studied, with the multi-resolution performance being tested. Inspecting the media of a highroad by analyzing the GPR data, with the abnormal characteristics being designated, validated the applicability of the proposed method.

scholarly journals Detection of Pneumatic Conveying by Acoustic Emissions

2019 ◽  
Vol 9 (3) ◽  
pp. 501
Author(s):  
Liansuo An ◽  
Weilong Liu ◽  
Yongce Ji ◽  
Guoqing Shen ◽  
Shiping Zhang
Keyword(s):  
Mean Squared Error ◽  
Acoustic Emissions ◽  
Ensemble Empirical Mode Decomposition ◽  
Wire Mesh ◽  
Pneumatic Conveying ◽  
Intrinsic Mode Functions ◽  
Energy Fraction ◽  
Flow Rates ◽  
Mode Decomposition ◽  
Ae Signal

The acoustic emission (AE) method is used in certain industries for the measurement of pneumatic conveying. Instead of the non-intrusive sensors, the comparison of two different intrusive probes in pneumatic conveying is presented in this work, and the AE signals generated by the flow for different particle flow rates and particle sizes were studied. Comparing the distribution of root mean square (RMS) values indicates that the AE signal acquired by a wire mesh probe was more reliable than that from a T-type probe. Limited intrinsic mode functions (IMFs) were extracted from the raw signals by the ensemble empirical mode decomposition (EEMD) algorithm. The characteristics of these signals were analyzed in both the time and frequency domains, and the energies of different IMFs were used to predict the particle mass flow rates, demonstrating a relative error under 10% achieved by the proposed monitoring system. Additionally, the mean squared error contribution fraction, instead of the energy fraction, can predict the particle size.

Gearbox Fault Detection Using Empirical Mode Decomposition

2004 ◽  
Author(s):  
Xianfeng Fan ◽  
Ming J. Zuo
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Original Data ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Stationary Vibration ◽  
Non Stationary Signal ◽  
Envelope Spectrum

Local faults in a gearbox cause impacts and the collected vibration signal is often non-stationary. Identification of impulses within the non-stationary vibration signal is key to fault detection. Recently, the technique of Empirical Mode Decomposition (EMD) was proposed as a new tool for analysis of non-stationary signal. EMD is a time series analysis method that extracts a custom set of bases that reflects the characteristic response of a system. The Intrinsic Mode Functions (IMFs) within the original data can be obtained through EMD. We expect that the change in the amplitude of the special IMF’s envelope spectrum will become larger when fault impulses are present. Based on this idea, we propose a new fault detection method that combines EMD with Hilbert transform. The proposed method is compared with both the Hilbert-Huang transform and the wavelet transform using simulated signal and real signal collected from a gearbox. The results obtained show that the proposed method is effective in capturing the hidden fault impulses.

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