Application of Empirical Mode Decomposition in Structural Health Monitoring: Some Experience (original) (raw)
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Structural health monitoring using empirical mode decomposition and the Hilbert phase
Journal of Sound and Vibration, 2006
This paper discusses a new signal processing tool involving the use of empirical mode decomposition and its application to health monitoring of structures. Empirical mode decomposition is a time-series analysis method that extracts a custom set of basis functions to describe the vibratory response of a system. In conjunction with the Hilbert Transform, the empirical mode decomposition method provides some unique information about the nature of the vibratory response. In this paper, the method is used to process time-series data from a variety of 1-D structures with and without structural damage. Empirically derived basis functions are processed through the Hilbert-Huang Transform to obtain magnitude, phase, and damping information. This magnitude, phase, and damping information is later processed to extract the underlying incident energy propagating through the structure. This incident energy is also referred to as the dereverberated response of a structure. Using simple physics-based models of 1-D structures, it is possible to determine the location and extent of damage by tracking phase properties between successive degrees of freedom. This paper also presents experimental validation of this approach using a civil building model. Results illustrate that this new time-series method is a powerful signal processing tool that tracks unique features in the vibratory response of structures.
arXiv: Signal Processing, 2018
Civil structures are on the verge of changing which leads energy dissipation capacity to decline. Structural Health Monitoring (SHM) as a process in order to implement a damage detection strategy and assess the condition of structure plays a key role in structural reliability. Earthquake is a recognized factor in variation of structures condition, inasmuch as inelastic behavior of a building subjected to design level earthquakes is plausible. In this study Hilbert Huang Transformation (HHT) is superseded by Ensemble Empirical Mode decomposition (EEMD) and Hilbert Transform (HT) together. Albeit analogous, EEMD brings more appropriate Intrinsic Mode Functions (IMFs) than Empirical Mode Decomposition (EMD). IMFs are employed to assess first mode frequency and mode shape. Afterward, Artificial Neural Networks (ANN) is applied to predict story acceleration based on acceleration of structure during previous moments. ANN functions precisely. Therefore, any congruency between predicted and...
A damage index for structural health monitoring based on the empirical mode decomposition
Journal of Mechanics of Materials and Structures, 2007
This paper presents two novel damage indices based on empirical mode decomposition (EMD) and fast Fourier integration for identifying structural damage caused by a change in structural stiffness. The paper also demonstrates the effectiveness of the proposed damage indices formulated based on a series of coupled mathematical/engineering approaches that are used to detect damage in pipes reliably and accurately. The main approach is based on monitoring the vibration response of pipes using piezoelectric sensors and the first intrinsic mode functions (IMFs). Finite element analysis is used to simulate the response of a healthy pipe, as well as pipes with various sizes of damage. Damages are meant to represent the outcome of local corrosion (damage) with varying reduction in areas around the circumference of the pipe. The evaluated damage indices could effectively establish the location of the defects. Moreover, the evaluated energy indices could also distinguish various size defects. To demonstrate further the effectiveness of our proposed damage indices, the results are compared with other effective indices based on wavelet packet and other statistical methods reported in the literature.
Journal of Zhejiang University SCIENCE A, 2013
Structural health monitoring (SHM) is a relevant topic for civil systems and involves the monitoring, data processing and interpretation to evaluate the condition of a structure, in order to detect damage. In real structures, two or more sites or types of damage can be present at the same time. It has been shown that one kind of damaged condition can interfere with the detection of another kind of damage, leading to an incorrect assessment about the structure condition. Identifying combined damage on structures still represents a challenge for condition monitoring, because the reliable identification of a combined damaged condition is a difficult task. Thus, this work presents a fusion of methodologies, where a single wavelet-packet and the empirical mode decomposition (EMD) method are combined with artificial neural networks (ANNs) for the automated and online identification-location of single or multiple-combined damage in a scaled model of a five-bay truss-type structure. Results showed that the proposed methodology is very efficient and reliable for identifying and locating the three kinds of damage, as well as their combinations. Therefore, this methodology could be applied to detection-location of damage in real truss-type structures, which would help to improve the characteristics and life span of real structures.
Journal of Civil Structural Health Monitoring, 2020
In this paper, a hybrid damage detection technique involving a combination of variational mode decomposition (VMD) and frequency domain decomposition (FDD) has been applied to study the effectiveness of damage detection in presence of heavily noise contaminated environment. Damage of small magnitude has been tested under Gaussian pulse noise ranging from 0 to 100% for damage analysis. FDD, a signal processing algorithm, has system identification capability in the presence of noise but requires the output acceleration data from all the sensors installed in the nodes of a structure to identify damage. To reduce the number of sensor data needed to identify damage, wavelet-based algorithms have been used to obtain intrinsic mode functions (IMFs) from single sensor output. These IMFs are then fed to FDD algorithm to obtain the natural frequencies of the structure. For comparison purpose, the algorithms (empirical mode decomposition (EMD) + FDD, and VMD + FDD) have been applied to ASCE benchmark building, which has been set as a common platform, using sensor data of first storey. It was observed that the VMD + FDD gives satisfactory damage identification results for 100% noise contamination whereas EMD + FDD was unable to identify damage accurately for noise above 20%. The robustness of VMD + FDD has been established for a different type of noise, random-valued impulse noise, applied on the benchmark structure for detecting the structural parameters. The hybrid algorithm was also checked for system identification using the sensor data of fourth storey to establish its robustness against the sensitivity of the sensor location.
Scientia Iranica
In order to implement a damage detection strategy and assess the condition of a structure, Structural Health Monitoring (SHM) as a process plays a key role in structural reliability. This paper aims to present a methodology for online detection of damages that may occur during a strong ground excitation. In this regard, Empirical Mode Decomposition (EMD) is superseded by Ensemble Empirical Mode Decomposition (EEMD) in the Hilbert Huang Transformation (HHT). Although analogous with EMD, EEMD brings about more appropriate Intrinsic Mode Functions (IMFs). IMFs are employed to assess the rst-mode frequency and mode shape. Afterwards, Arti cial Neural Network (ANN) is applied to predict story acceleration based on previously measured values. Because ANN functions precisely, any congruency between predicted and measured accelerations indicates the onset of damage. Then, another ANN method is applied to estimate the sti ness matrix. Though the rst-mode shape and frequency are calculated in advance, the process essentially requires an inverse problem to be solved in order to nd sti ness matrix, which is done by ANN. This algorithm is implemented on moment-resisting steel frames, and the results show the reliability of the proposed methodology for online prediction of structural damage.
Damage detection of structures using signal processing and artificial neural networks
Advances in Structural Engineering, 2019
This article presents a signal-based seismic structural health monitoring technique for damage detection and evaluating damage severity of a multi-story frame subjected to an earthquake event. As a case study, this article is focused on IASC–ASCE benchmark problem to provide the possibility for side-by-side comparison. First, three signal processing techniques including empirical mode decomposition, Hilbert vibration decomposition, and local mean decomposition, categorized as instantaneous time–frequency methods, have been compared to find a method with the best resolution in extracting frequency responses. Time-varying single degree of freedom and multiple degree of freedom models are used since real vibration signals are nonstationary and nonlinear in nature. Based on the results, empirical mode decomposition has proved to outperform than the others. Second, empirical mode decomposition is used to extract the acceleration response of the sensors. Next, a two-stage artificial neura...
Journal of Structural Integrity and Maintenance, 2019
Extensive research has been carried out on vibration-based structural health monitoring in the last few decades. A large number of these studies focus on response-based techniques due to its ease and efficiency. The main concern in such investigations is to extract a proper damage indicative feature from response data. This paper presents a new scheme in decomposing response data in order to extract a structural damage feature. Individual points on the structure body have their own time response signal. All these signals decomposed simultaneously through Multi-channel Empirical Mode Decomposition. Decomposed data of all structural points are put together to form a virtual structural deflection shape over time for each decomposed base vector. These time dependent deflection shapes are employed as a feature to determine damage location, if any. The proposed method was implemented both numerically and experimentally. In all cases, the proposed technique was able to locate the damaged zone successfully.
Bivariate empirical mode decomposition and its contribution to wind turbine condition monitoring
Journal of Sound and Vibration, 2011
Grinding chatter reduces the long-term reliability of grinding machines. Detecting the negative effects of chatter requires improved chatter detection techniques. The vibration signals collected from grinders are mainly nonstationary, nonlinear and multidimensional. Hence, bivariate empirical mode decomposition (BEMD) has been investigated as a multiple signal processing method. In this paper, a feature vector extraction method based on BEMD and Hilbert transform was applied to the problem of grinding chatter. The effectiveness of this method was tested and validated with a simulated chatter signal produced by a vibration signal generator. The extraction criterion of true intrinsic mode functions (IMFs) was also investigated, as well as a method for selecting the most ideal number of projection directions using the BEMD algorithm. Moreover, real-time variance and instantaneous energy were employed as chatter feature vectors for improving the prediction of chatter. Furthermore, the combination of BEMD and Hilbert transform was validated by experimental data collected from a computer numerical control (CNC) guideway grinder. The results reveal the good behavior of BEMD in terms of processing nonstationary and nonlinear signals, and indicating the synchronous characteristics of multiple signals. Extracted chatter feature vectors were demonstrated to be reliable predictors of early grinding chatter.
Empirical mode decomposition (EMD) is a relatively new form of time series analysis which is developed to decompose a signal into Intrinsic Mode Functions (IMFs). In this work, EMD is introduced and key aspects of its application in detection of a crack in a rectangular steel plate by analyzing the natural frequencies are proposed. This method helps to analyze a non-stationary signal.Comparative study of various signal processing techniques such as Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT) and Empirical Mode Decomposition (EMD) has been performed to analyze changes in modal characteristics in order to detect a crack in a rectangular steel plate. The natural frequencies of cracked and uncracked plates obtained by EMD are also compared with Finite Element Method (FEM) results. Experiments are performed where fixed plates are excited and time domain vibration data are acquired to employ signal processing techniques.