scholarly journals Multivariate empirical mode decomposition–based structural damage localization using limited sensors

2021 ◽  
pp. 107754632110069
Author(s):  
Sandeep Sony ◽  
Ayan Sadhu
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Cost Effective ◽  
Threshold Value ◽  
Damage Localization ◽  
Time Frequency ◽  
Multivariate Empirical Mode Decomposition ◽  
Percentage Difference ◽  
Mode Decomposition

In this article, multivariate empirical mode decomposition is proposed for damage localization in structures using limited measurements. Multivariate empirical mode decomposition is first used to decompose the acceleration responses into their mono-component modal responses. The major contributing modal responses are then used to evaluate the modal energy for the respective modes. A damage localization feature is proposed by calculating the percentage difference in the modal energies of damaged and undamaged structures, followed by the determination of the threshold value of the feature. The feature of the specific sensor location exceeding the threshold value is finally used to identify the location of structural damage. The proposed method is validated using a suite of numerical and full-scale studies. The validation is further explored using various limited measurement cases for evaluating the feasibility of using a fewer number of sensors to enable cost-effective structural health monitoring. The results show the capability of the proposed method in identifying as minimal as 2% change in global modal parameters of structures, outperforming the existing time–frequency methods to delineate such minor global damage.

scholarly journals Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition

2019 ◽  
Author(s):  
Andrés Felipe Soler ◽  
Pablo A. Muñoz-Gutiérrez ◽  
Maximiliano Bueno-López ◽  
Eduardo Giraldo ◽  
Marta Molinas
Keyword(s):  
Empirical Mode Decomposition ◽  
Neural Activity ◽  
Low Density ◽  
Multiple Channel ◽  
Time Frequency ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Channel Analysis ◽  
The Mean ◽  
Mixing Problem

AbstractSeveral approaches can be used for estimating neural activity. The main differences between them are in the apriori information used and their sensibility to high noise levels. Empirical Mode Decomposition (EMD) has been recently applied to Electroencephalography EEG-based neural activity reconstruction to provide apriori time-frequency information to improve the neural activity estimation. EMD has the specific ability to identify independent oscillatory modes in non-stationary signals with multiple oscillatory components. The various attempts to use EMD in EEG analysis, however, did not provide yet the best reconstructions due to the intrinsic mode mixing problem of EMD. Some previous works have used a single-channel analysis and in other cases, multiple-channel have been used for other applications. In this paper, we present a study about multiple-channel analysis using Multivariate Empirical Mode Decomposition (MEMD) as a method to attenuate the mode mixing problem and to provide apriori useful time-frequency information to the reconstruction of neuronal activity using several low-density EEG electrode montages. The methods were evaluated over real and synthetic EEG data, in which the reconstructions were performed using multiple sparse priors (MSP) method with several electrode numbers of 32, 16, and 8, and the source reconstruction quality was measured using the Wasserstein Metric. Comparing the solutions when no pre-processing was made and when MEMD was applied, the source reconstructions were improved using MEMD as apriori information in the low-density montage of 8 and 16 electrodes. The mean source reconstruction error on a real EEG dataset was reduced a 59.42% and 66.04% for the 8 and 16 electrodes montages respectively, and on a simulated EEG with three active sources, the mean error was reduced an 87.31% and 31.45% for the same electrodes montages.

Energy-based damage localization under ambient vibration and non-stationary signals by ensemble empirical mode decomposition and Mahalanobis-squared distance

2019 ◽  
Vol 26 (11-12) ◽  
pp. 1012-1027 ◽  
Author(s):  
Hassan Sarmadi ◽  
Alireza Entezami ◽  
Mohammadhassan Daneshvar Khorram
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Ensemble Empirical Mode Decomposition ◽  
Ambient Vibration ◽  
Damage Localization ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Stationary Vibration

Damage localization of damaged structures is an important issue in structural health monitoring. In data-based methods based on statistical pattern recognition, it is necessary to extract meaningful features from measured vibration signals and utilize a reliable statistical technique for locating damage. One of the challenging issues is to extract reliable features from non-stationary vibration signals caused by ambient excitation sources. This article proposes a new energy-based method by using ensemble empirical mode decomposition and Mahalanobis-squared distance to obtain energy-based multivariate features and locate structural damage under ambient vibration and non-stationary signals. The main components of the proposed method include extracting intrinsic mode functions of vibration signals by ensemble empirical mode decomposition, choosing adequate and optimal intrinsic mode functions, partitioning the selected intrinsic mode functions at each sensor into segments with the same dimensions, calculating the intrinsic mode function energy at each segment, preparing energy-based multivariate features at each sensor, computing Mahalanobis-squared distance values, and obtaining a vector of average Mahalanobis-squared distance quantities of all sensors. The major contributions of the proposed method consist of proposing an innovative non-parametric strategy for feature extraction, presenting generalized Pearson correlation function for the selection of optimal intrinsic mode functions, using a simple and effective segmentation algorithm, and applying energy-based features to the process of damage localization. The main advantage of the proposed method is its great applicability to locating single and multiple damage cases. The measured vibration responses of the well-known IASC-ASCE structure are applied to verify the effectiveness and reliability of the proposed energy-based method along with several comparative studies. Results will demonstrate that this approach is highly capable of locating damage under stationary and non-stationary vibration signals attributable to ambient excitations.

Structural damage localization through multi-channel empirical mode decomposition

2019 ◽  
Vol 10 (1) ◽  
pp. 102-117 ◽  
Author(s):  
Mehdi Salehi ◽  
Mansour Azami
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Flexural Rigidity ◽  
Structural Vibration ◽  
Damage Localization ◽  
Content Type ◽  
Vibrational Response ◽  
Response Data ◽  
Mode Decomposition ◽  
Complicated Geometries

Purpose The purpose of this paper is to develop a new structural damage detection technique based on multi-channel empirical mode decomposition (MEMD) of vibrational response data. Design/methodology/approach Empirical mode decomposition (EMD) is an empirical data-based signal decomposition method which has been applied in many engineering problems. Utilizing classical EMD to reveal the damage-indicating features of structural vibration response encounters some difficulties due to the inconsistency of modes obtained from different data channels. To overcome this problem, MEMD has been employed. To this end, MEMD algorithm has been adopted to impulse response vector of measured DOFs. The proposed method has been carried out concerning both numerical and experimental beam models. Damage has been modeled by reducing the flexural rigidity in some predefined beam sections. The effects of various factors such as measurement grid density, damage severity and damage position are investigated. Findings The results of both numerical and experimental case studies have been promising. The method could determine the damage location in all cases. The efficiency of method gets better when damage is located far from inflation points of the corresponding mode. In such cases, utilizing higher modes can make up the efficiency. Research limitations/implications Since the present research is the first investigation of MEMD in damage localization, just one-dimensional structures have been studied. Extending the method to more complicated geometries needs further attempt. Originality/value Although a number of relevant studies have been carried out based on EMD, up to the author’s best knowledge, this is the first attempt to structural damage localization using MEMD.

Multivariate empirical mode decomposition

2009 ◽  
Vol 466 (2117) ◽  
pp. 1291-1302 ◽  
Author(s):  
N. Rehman ◽  
D. P. Mandic
Keyword(s):  
Empirical Mode Decomposition ◽  
Human Motion ◽  
Time Frequency ◽  
Quasi Monte Carlo ◽  
Multivariate Empirical Mode Decomposition ◽  
Low Discrepancy Sequences ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Local Mean ◽  
Multivariate Signals

Despite empirical mode decomposition (EMD) becoming a de facto standard for time-frequency analysis of nonlinear and non-stationary signals, its multivariate extensions are only emerging; yet, they are a prerequisite for direct multichannel data analysis. An important step in this direction is the computation of the local mean, as the concept of local extrema is not well defined for multivariate signals. To this end, we propose to use real-valued projections along multiple directions on hyperspheres ( n -spheres) in order to calculate the envelopes and the local mean of multivariate signals, leading to multivariate extension of EMD. To generate a suitable set of direction vectors, unit hyperspheres ( n -spheres) are sampled based on both uniform angular sampling methods and quasi-Monte Carlo-based low-discrepancy sequences. The potential of the proposed algorithm to find common oscillatory modes within multivariate data is demonstrated by simulations performed on both hexavariate synthetic and real-world human motion signals.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

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