Weighted Average Instantaneous Frequency Extraction via Time-frequency Analysis

2017 ◽  
Author(s):  
N.H. Liu ◽  
J.H. Gao ◽  
Q. Wang
Keyword(s):  
Frequency Analysis ◽  
Instantaneous Frequency ◽  
Weighted Average ◽  
Time Frequency Analysis ◽  
Time Frequency

scholarly journals A Time-Frequency Research for Ultrasonic Guided Wave Generated from the Debonding Based on a Novel Time-Frequency Analysis Technique

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Junhua Wu ◽  
Zheshu Ma ◽  
Yonghui Zhang
Keyword(s):  
Carbon Fibre ◽  
Frequency Analysis ◽  
Instantaneous Frequency ◽  
Fibre Composite ◽  
Guided Wave ◽  
Carbon Fibre Composite ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Ultrasonic Guided Wave ◽  
Carbon Fibre Composites

Carbon fibre composites have a promising application in the future of the vehicle, because of their high strength and light weight. Debonding is a major defect of the carbon fibre composite. The time-frequency analysis is fundamental to identify the defect on ultrasonic nondestructive evaluation and testing. In order to obtain the instantaneous frequency and the peak time of modes of the ultrasonic guided wave, an algorithm based on the Smoothed Pseudo Wigner-Ville distribution and the peak-track algorithm is presented. In the algorithm, a masking step is proposed, which can guarantee that the peak-track algorithm can automatically exact the instantaneous frequency and the instantaneous amplitude of different modes on the Smoothed Pseudo Wigner-Ville distribution. An experiment for detecting the debonding for a type of carbon fibre composite is done. The presented algorithm is employed on the experimental signals. The processed result of experimental signals reveals that the defect can stimulate new modes, and there is a quantitative relationship between the defect size and the frequency of the new mode. The presented technique provides a valuable way to detect the presentence, calculate the size, and locate the position of the debonding defect.

Time-frequency analysis of deep crustal reflection seismic data using Wigner-Ville distributions

2001 ◽  
Vol 38 (7) ◽  
pp. 1027-1035 ◽  
Author(s):  
Kris Vasudevan ◽  
Frederick A Cook
Keyword(s):  
Frequency Analysis ◽  
Instantaneous Frequency ◽  
Seismic Data ◽  
Drill Hole ◽  
Lateral Variation ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Reflection Seismic ◽  
Instantaneous Frequencies ◽  
Crustal Reflection

One important component of deep crustal reflection seismic data in the absence of drill-hole data and surface-outcrop constraints is classifying and quantifying reflectivity patterns. One approach to this component uses a recently developed data-decomposition technique, seismic skeletonization. Skeletonized coherent events and their attributes are identified and stored in a relational database, allowing easy visualization and parameterization of the reflected wavefield. Because one useful attribute, the instantaneous frequency, is difficult to derive within the current framework of skeletonization, time–frequency analysis and a new method, empirical mode skeletonization, are used to derive it. Other attributes related to time–frequency analysis that can be derived from the methods can be used for shallow and deep reflection seismic interpretation and can supplement the seismic attributes accrued from seismic skeletonization. Bright reflections observed from below the sedimentary basin in the Southern Alberta Lithosphere Transect have recently been interpreted to be caused by highly reflective sills. Time–frequency analysis of one of these reflections shows the lateral variation of energy with instantaneous frequency for any given time and the lateral variation of energy with time for any instantaneous frequency. Results from empirical mode skeletonization for the same segment of data illustrate the differences in the instantaneous frequencies among the intrinsic modes of the data. Thus, time–frequency distribution of amplitude or energy for any signal may be a good indicator of compositional differences that can vary from one location to another.

PCA and ICA Based Prognostic Health Monitoring of Electronic Assemblies Subjected to Simultaneous Temperature-Vibration Loads

2017 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas
Keyword(s):  
Frequency Analysis ◽  
Health Monitoring ◽  
Instantaneous Frequency ◽  
Principal Component ◽  
Component Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Strain Components ◽  
Frequency Components ◽  
Electronic Assemblies

This paper focusses on health monitoring of electronic assemblies under vibration load of 14 G until failure at an ambient temperature of 55 degree Celsius. Strain measurements of the electronic assemblies were measured using the voltage outputs from the strain gauges which are fixed at different locations on the Printed Circuit Board (PCB). Various analysis was conducted on the strain signals include Time-frequency analysis (TFA), Joint Time-Frequency analysis (JTFA) and Statistical techniques like Principal component analysis (PCA), Independent component analysis (ICA) to monitor the health of the packages during the experiment. Frequency analysis techniques were used to get a detailed understanding of the different frequency components before and after the failure of the electronic assemblies. Different filtering algorithms and frequency quantization techniques gave insight about the change in the frequency components with the time of vibration and the energy content of the strain signals was also studied using the joint time-frequency analysis. It is seen that as the vibration time increases the occurrence of new high-frequency components increases and further the amplitude of the high-frequency components also has increased compared to the before failure condition. Statistical techniques such as PCA and ICA were primarily used to reduce the dimensions of the larger data sets and provide a pattern without losing the different characteristics of the strain signals during the course of vibration of electronic assemblies till failure. This helps to represent the complete behavior of the electronic assemblies and to understand the change in the behavior of the strain components till failure. The principal components which were calculated using PCA discretely separated the before failure and after failure strain components and this behavior were also seen by the independent components which were calculated using the Independent Component Analysis (ICA). To quantify the prognostics and hence the health of the electronic assemblies, different statistical prediction algorithms were applied to the coefficients of principal and independent components calculated from PCA and ICA analysis. The instantaneous frequency of the strain signals was calculated and PCA and ICA analysis on the instantaneous frequency matrix was done. The variance of the principal components of instantaneous frequency showed an increasing trend during the initial hours of vibration and after attaining a maximum value it then has a decreasing trend till before failure. During the failure of components, the variance of the principal component decreased further and attained a lowest value. This behavior of the instantaneous frequency over the period of vibration is used as a health monitoring feature.

scholarly journals An Improved Time-Frequency Analysis Method for Instantaneous Frequency Estimation of Rolling Bearing

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Zengqiang Ma ◽  
Wanying Ruan ◽  
Mingyi Chen ◽  
Xiang Li
Keyword(s):  
Frequency Analysis ◽  
Instantaneous Frequency ◽  
Frequency Estimation ◽  
Rolling Bearing ◽  
Analysis Method ◽  
Instantaneous Frequency Estimation ◽  
Time Frequency Analysis ◽  
Noise Interference ◽  
Time Frequency ◽  
Frequency Representation

Instantaneous frequency estimation of rolling bearing is a key step in order tracking without tachometers, and time-frequency analysis method is an effective solution. In this paper, a new method applying the variational mode decomposition (VMD) in association with the synchroextracting transform (SET), named VMD-SET, is proposed as an improved time-frequency analysis method for instantaneous frequency estimation of rolling bearing. The SET is a new time-frequency analysis method which belongs to a postprocessing procedure of the short-time Fourier transform (STFT) and has excellent performance in energy concentration. Considering nonstationary broadband fault vibration signals of rolling bearing under variable speed conditions, the time-frequency characteristics cannot be obtained accurately by SET alone. Thus, VMD-SET method is proposed. Firstly, the signal is decomposed into several intrinsic mode functions (IMFs) with different center frequency by VMD. Then, effective IMFs are selected by mutual information and kurtosis criteria and are reconstructed. Next, the SET method is applied to the reconstructed signal to generate the time-frequency representation with high resolution. Finally, instantaneous frequency trajectory can be accurately extracted by peak search from the time-frequency representation. The proposed method is free from time-varying sidebands and is robust to noise interference. It is proved by numerical simulated signal analysis and is further validated by lab experimental rolling bearing vibration signal analysis. The results show this method can estimate the instantaneous frequency with high precision without noise interference.

A machine learning–based approach for adaptive sparse time–frequency analysis used in structural health monitoring

2020 ◽  
Vol 19 (6) ◽  
pp. 1963-1975 ◽  
Author(s):  
Yuequan Bao ◽  
Yibing Guo ◽  
Hui Li
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Structural Health Monitoring ◽  
Frequency Analysis ◽  
Health Monitoring ◽  
Instantaneous Frequency ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Structural Health ◽  
Reconstructed Signal

Time–frequency analysis is an essential subject in nonlinear and non-stationary signal processing in structural health monitoring, which can give a clear illustration of the variation trend of time-varying parameters. Thus, it plays a significant role in structural health monitoring, such as data analysis, and nonlinear damage detection. Adaptive sparse time–frequency analysis is a recently developed method used to estimate an instantaneous frequency, which can achieve high-resolution adaptivity by looking for the sparsest time–frequency representation of the signal within the largest possible time–frequency dictionary. However, in adaptive sparse time–frequency analysis, non-convex least-square optimization is the most important and difficult part of the algorithm; therefore, in this research the powerful optimization capabilities of machine learning were employed to solve the non-convex least-square optimization and achieve the accurate estimation of the instantaneous frequency. First, the adaptive sparse time–frequency analysis was formalized into a machine-learning task. Then, a four-layer neural network was designed, the first layer of which was used for training the coefficients of the envelope of each basic functions in a linear space. The next two merge layers were used to solve the complex calculation in a neural network. Finally, the real and imaginary parts of the reconstructed signal were the outputs of the output layer. The optimal weights in this designed neural network were trained and optimized by comparing the output reconstructed signal with the target signal, and a stochastic gradient descent optimizer was used to update the weights of the network. Finally, the numerical examples and experimental examples of a cable model were employed to illustrate the ability of the proposed method. The results show that the proposed method which is called neural network–adaptive sparse time–frequency analysis can give accurate identification of the instantaneous frequency, and it has a better robustness to initial values when compared with adaptive sparse time–frequency analysis.

Instantaneous frequency in time–frequency analysis: Enhanced concepts and performance of estimation algorithms

2014 ◽  
Vol 35 ◽  
pp. 1-13 ◽  
Author(s):  
Ljubiša Stanković ◽  
Igor Djurović ◽  
Srdjan Stanković ◽  
Marko Simeunović ◽  
Slobodan Djukanović ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Instantaneous Frequency ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Estimation Algorithms ◽  
And Performance

Time-Frequency Analysis of Non-Stationary Ground Motions via Multivariate Empirical Mode Decomposition

2014 ◽  
Vol 580-583 ◽  
pp. 1734-1741
Author(s):  
Yan Wen Su ◽  
Guo Qing Huang ◽  
Liu Liu Peng
Keyword(s):  
Frequency Analysis ◽  
Instantaneous Frequency ◽  
Stationary Process ◽  
Ground Motions ◽  
Analysis Framework ◽  
Spectral Evolution ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition

In this paper, MEMD-based scalogram and coscalogram, and instantaneous frequency spectral are proposed to characterize the data derived from the multivariate non-stationary process. The scalogram and instantaneous frequency spectral capture spectral evolution of each component while the coscalogram reveals embedded intermittent correlation between two components. Compared with scale-based scalogram and coscalogram, frequency-based instantaneous frequency spectral provides more detailed portrayal for multivariate data. The effectiveness of the proposed MEMD-based time-frequency analysis framework is validated by numerical examples of uniformly and generally modulated ground motions.

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