A novel singular spectrum analysis–based baseline-free approach for fatigue-breathing crack identification

2018 ◽  
Vol 29 (10) ◽  
pp. 2249-2266 ◽  
Author(s):  
J Prawin ◽  
K Lakshmi ◽  
A Rama Mohan Rao
Keyword(s):  
Spectrum Analysis ◽  
Crack Detection ◽  
Singular Spectrum Analysis ◽  
Experimental Studies ◽  
Damage Index ◽  
Fatigue Loading ◽  
Crack Identification ◽  
Breathing Crack ◽  
Singular Spectrum ◽  
Nonlinear Harmonics

Breathing cracks are the most common type of damages that occur in structures subjected to fatigue loading. These breathing cracks induce nonlinearity in the dynamic signatures of the cracked structure which carry useful information about the damage. In this article, we present a new baseline-free algorithm using singular spectrum analysis for breathing crack detection, localization, and characterization. The major advantage of using singular spectrum analysis is that it has the ability to reliably extract the nonlinear harmonics and intermodulation components buried in the noisy components of the response of the structure with breathing crack. A new damage index based on singular spectrum analysis exploiting the nonlinear harmonics and intermodulations in the response is proposed. Numerical simulation studies are carried out to evaluate the proposed damage diagnostic algorithm and later complemented with experimental studies to demonstrate its practical application.

Reference-Free Breathing Crack Identification of Beam-Like Structures Using an Enhanced Spatial Fourier Power Spectrum with Exponential Weighting Functions

2019 ◽  
Vol 19 (02) ◽  
pp. 1950017 ◽  
Author(s):  
J. Prawin ◽  
A. Rama Mohan Rao
Keyword(s):  
Fatigue Crack ◽  
Power Spectrum ◽  
Crack Detection ◽  
Crack Problem ◽  
Experimental Studies ◽  
Crack Identification ◽  
Successful Implementation ◽  
Breathing Crack ◽  
Fourier Power Spectrum ◽  
Exponential Weighting

Detection of incipient damage of structures at the earliest possible stage is desirable for successful implementation of any health monitoring system. In this paper, we focus on breathing crack problem and present a new reference-free algorithm for fatigue crack detection, localization, and characterization for beam-like structures. We use the spatial curvature of the Fourier power spectrum as a damage sensitive feature for fatigue crack identification. An exponential weighting function that takes into account nonlinear dynamic signatures, such as sub- and superharmonics, is proposed in the Fourier power spectrum in order to enrich the damage-sensitive features of the structure. Both numerical and experimental studies have been carried out to test and verify the proposed algorithm.

Frequency Comparison Function Method for Real-Time Identification of Breathing Crack at Welding Joint

2020 ◽  
Vol 20 (13) ◽  
pp. 2041001
Author(s):  
Xin Wang ◽  
Nan Wu ◽  
Quan Wang
Keyword(s):  
Real Time ◽  
Crack Detection ◽  
Experimental Studies ◽  
High Sensitivity ◽  
Crack Identification ◽  
Comparison Function ◽  
Amplitude Curve ◽  
Breathing Crack ◽  
Welding Joint ◽  
Frequency Comparison

In this research, the frequency comparison function (FCF) method is proposed and studied to realize high-sensitive real-time crack identification at the welding joint area for a beam-type structure. This method is derived from the frequency response function (FRF). During FCF, we use the response signal collected from the designated point of the structure instead of the excitation. The standard deviation of the FCF amplitude curve is calculated to detect and evaluate the possible crack and its induced vibration perturbations afterward. Vibration responses are simulated in ANSYS by the use of the finite element analysis of a welded beam structure, and these signals are then analyzed with the FCF algorithm. It is concluded that FCF is an efficient method for breathing crack identification and can be easily applied under different excitation conditions, including harmonic and random ones. Meanwhile, FCF can be applied without any pre-processing algorithms such as filtering and smoothing. So, it can be used for real-time crack identification. By combining the FCF with the smart coating sensor composed of piezoelectric layers, the crack identification with high sensitivity is realized. The crack is detectable at its very early stage (starting from 3% of the beam thickness). Experimental studies under harmonic and random excitations are processed, and the results prove high sensitivity and feasibility of the proposed crack detection method.

Vibration-based breathing crack identification using non-linear intermodulation components under noisy environment

2019 ◽  
Vol 19 (1) ◽  
pp. 86-104 ◽  
Author(s):  
J Prawin ◽  
A Rama Mohan Rao
Keyword(s):  
Spectral Analysis ◽  
Crack Detection ◽  
Damage Index ◽  
Measurement Noise ◽  
Experimental Simulation ◽  
Crack Identification ◽  
Diagnostic Process ◽  
Breathing Crack ◽  
Spectral Correlation ◽  
Non Linear

Structural damages can result in non-linear dynamical signatures such as lower and higher order harmonics and signal modulation that can significantly enhance their detection. The conventional spectral analysis is used in most existing vibration-based damage diagnostic techniques to extract these damage-sensitive non-linear features. However, the major limitation of using spectral analysis is that the amplitudes of non-linear harmonics are highly sensitive to measurement noise and may mislead the damage diagnostic process. Keeping this in view, we present a new reference-free damage diagnostic technique for fatigue-breathing crack detection, localization and characterization using the cyclic spectral analysis-based technique. A new damage index based on spectral correlation exploiting the non-linear intermodulation in the response is proposed. The proposed cyclic spectral analysis-based diagnostics are highly immune to the measurement noise. Numerical and experimental simulation studies have been carried out by considering a beam with single and multiple breathing cracks, to test and verify the robustness and effectiveness of the proposed technique.

scholarly journals Multi-Channel Singular Spectrum Analysis on Geocenter Motion and Its Precise Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1403
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Solid Earth ◽  
Spectrum Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Autoregressive Moving Average Model ◽  
Seasonal Characteristics ◽  
Geocenter Motion ◽  
Moving Average Model

Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.

Application of singular spectrum analysis in reconstruction of the annual signal from GRACE

2020 ◽  
Vol 14 (3) ◽  
pp. 295-302
Author(s):  
Chuandong Zhu ◽  
Wei Zhan ◽  
Jinzhao Liu ◽  
Ming Chen
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Mixture Effect ◽  
Singular Spectrum ◽  
Grace Data ◽  
Terrestrial Water ◽  
Annual Signal ◽  
Long Term Variations

AbstractThe mixture effect of the long-term variations is a main challenge in single channel singular spectrum analysis (SSA) for the reconstruction of the annual signal from GRACE data. In this paper, a nonlinear long-term variations deduction method is used to improve the accuracy of annual signal reconstructed from GRACE data using SSA. Our method can identify and eliminate the nonlinear long-term variations of the equivalent water height time series recovered from GRACE. Therefore the mixture effect of the long-term variations can be avoided in the annual modes of SSA. For the global terrestrial water recovered from GRACE, the peak to peak value of the annual signal is between 1.4 cm and 126.9 cm, with an average of 11.7 cm. After the long-term and the annual term have been deducted, the standard deviation of residual time series is between 0.9 cm and 9.9 cm, with an average of 2.1 cm. Compared with the traditional least squares fitting method, our method can reflect the dynamic change of the annual signal in global terrestrial water, more accurately with an uncertainty of between 0.3 cm and 2.9 cm.

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