Indicator For Damage Detection Research Articles

Frequency Identification and Damage Detection of Bridges Using Virtual Contact-Point Responses of Railway Vehicle

This paper proposed an indirect bridge frequency identification and damage detection method based on the virtual contact-point (CP) responses of railway vehicles moving over the bridge. The virtual CP response is acquired to reduce the inverse effects of the track irregularity and vehicle dynamic components on the indirect bridge monitoring of the vehicle–bridge interaction (VBI) system. By combining the virtual CP responses of multiple connected vehicles, the first three bridge frequencies can be identified when vehicles run at high speed. The synchrosqueezing transform (SST) is then adopted to extract bridge-related dynamic components from the virtual CP response for bridge modal identification. The time-frequency (TF) representation of the virtual CP response using SST is further analyzed for bridge damage detection. The spectral entropy (SE) of the TF representation is introduced to quantify TF changes and serves as a damage index for bridge damage localization. Numerical studies are conducted to investigate the effects of measurement noise, damage conditions, and track irregularity on the effectiveness of the proposed method. The results demonstrate that SE is a good indicator for damage detection, and that the proposed method combined with wavelet denoising has good anti-noise performance and can well locate bridge damage using virtual CP response.

On the use of multivariate autoregressive models for vibration-based damage detection and localization

This paper proposes a novel method suitable for vibration-based damage identification of civil structures and infrastructures under ambient excitation. The damage-sensitive feature employed in the presented algorithm consists of a vector of multivariate autoregressive parameters estimated from the vibration responses collected at different locations of the analyzed structure. Outlier analysis and statistical pattern recognition are exploited for damage detection and localization. In particular, the Mahalanobis distance between a set of reference (i.e., “healthy”) and inspection parameters is evaluated. A threshold is then selected to determine whether the inspection vectors refer to damaged or undamaged conditions. The effectiveness of the proposed approach is proved using numerical simulations and experimental data from a benchmark test. The analysis results show that the largest values of Mahalanobis distance can be found in the proximity of those sensors closest to the damaged elements. Thus, the Mahalanobis distance applied to vectors of multivariate autoregressive parameters has proven to be a robust indicator for damage detection and localization.

Development of robust metric based on cumulative electrical power for electromechanical impedance based structural health monitoring

Electromechanical impedance (EMI) based techniques have been proposed for structural health monitoring due to their sensitivity to low levels of damage. Most of the work in the EMI technique depends on the change in the admittance signature of the structure in the healthy and damaged state. Several metrics have been proposed to quantify this difference in the signature. Most common being root-mean square difference (RMSD), mean absolute percentage deviation, correlation coefficient etc. As the admittance signatures has several troughs and peaks, the statistical metrics are not robust and often show false detection due to ambient changes and measurement noise.Thus, this paper proposes a novel index for the damage detection using the EMI technique based on the cumulative electrical power. The frequency v/s resistance or conductance plot is used for calculating the normalized cumulative electrical power (NCP) of the system. The NCP curve is a monotonically increasing function and hence robust for statistical comparison. The cumulative power curve is then used to develop three different indices comparing the amplitude difference (RMSD of the NCP curves), difference in the area under the NCP curve as well as the modified Frechet distance between the NCP curves. The performance of these indices are compared with the RMSD index which has been commonly used. The comparison is carried out on four different structures and show very encouraging results. In addition to the experimental validation, sensitivity studies have been carried out on an analytical signal. It is seen that the Frechet distance based index is a robust indicator for damage detection and minimizes the false detection under variety of conditions affecting the EMI signature.

Novel Damage Detection Tool Based on Load Path Analysis Using Ustar (U*)

A novel tool for damage detection of an engineering structure based on the load path analysis using U* index is developed with high sensitivity. U* is a math index presentation of the internal stiffness distribution of a structure subjected to a certain load. The global variation of the stiffness distribution will occur due to the damage and can be measured by U* index as a new damage detection indicator. U* index theory is introduced first in the paper, and the damage detection mechanism and feasibility are then described, explained and studied through two sample structures by finite element analysis (FEA). Through the case studies, it is noted that good damage sensitivity can be realized even when the measurement position is far (30% of the whole structure size) away from the damage. The experimental study further validates the effectiveness and feasibility of U* measurement in damage detection. The U* index can be used as a new indicator for structural damage detection at an arbitrary measurement location with higher sensitivity than the stress analysis, which is only sensitive when the testing point is very close to the damage.

Damage Detection in Four Point Bending Test on Benchmark RC Structure Using Feature based Fusion

Sensor fusion has attracted significant attention in recent years due to its usability for many applications in real life. This encourages to use the fusion technique for damage detection in concrete structures. The major diffculty in a concrete damage detection lies in the fact of early crack detection and take proper action before the propagation of cracks. Therefore, different techniques were performed on a benchmark RC beam, which was subjected to four point loading. Then the features from multiple sensors were fused for early crack detection. In this framework, we first represent each measurement technique separately, in which coefficient of peak to peak amplitude from ultrasonic measurement, and the strain measured by strain gauges serve as the features indicator for damage detection. Canonical correlation analysis (CCA) is then applied to both features to construct a combination of features of peak to peak and strain matrices. The result indicates the possibility of using a feature-based fusion algorithm more robustly, and it increases the damage detection probability by reducing false alarm ratio.

A Novel Damage Indicator Based on the Electromechanical Impedance Principle for Structural Damage Identification.

This paper presents a novel structural damage detection indicator, i.e., fourth-order voltage statistical moment (FVSM) based on the electromechanical impedance (EMI) principle, and then proposes a two-step damage detection method based on the novel indicator and a differential evolution algorithm (DEA). In this study, several lead zirconate titanate (PZT) sensors bonded to an experimental steel beam were utilized to acquire the time-domain voltage responses. On this basis, the fourth-order voltage statistical moments (FVSMs) of the voltage responses are computed to locate the damage element in the detected structure, and the proposed damage detection method is utilized to quantify the damage. In addition, theoretical PZT voltage responses are also calculated based on the piezoelectric theory and the spectral element method (SEM). Experimental results verify the accuracy of the theoretical voltage values and the effectiveness of the proposed damage indicator. Results indicate that the FVSM is effective in locating the damage element. Integrated with DEA, the proposed technique is capable of quantifying damage.

Detection of structural changes through principal component analysis and multivariate statistical inference

This article introduces a new methodology for the detection of structural changes using a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at different actuation phases. The three main features that characterize the proposed methodology are (a) the nature of the data used in the test since vectors of principal component analysis projections are used instead of the entire measured response of the structure or the coefficients of an AutoRegressive model, (b) the number of data used since the test is based on two random samples instead of some characteristic indicators, and (c) the samples come from a multidimensional variable and therefore a test for the plausibility of a value for a normal population mean vector is performed. The framework of multivariate statistical inference is used with the objective of the classification of structures in healthy or damaged. The novel scheme for damage detection presented in this article —based on multivariate inference over the principal component analysis projections of the raw data—is applied, validated, and tested on a small aluminum plate. The results show that the presented methodology is able to accurately detect damages, that is, for each actuation phase, a unique and reliable damage detection indicator is obtained no matter the number of sensors and/or actuators. It is worth noting that a major contribution of this article is that there exists an entire range of significance levels where the multivariate statistical inference is able to offer a correct decision although all of the univariate tests make a wrong decision.

A simple damage detection indicator using operational deflection shapes

Catastrophic structural failure of aircrafts, bridges, buildings and other structures in modern societies has always been of primary concern because of the loss of human lifes and of negative economic impact. The aging of the structures, the growing dependency on their role in our networks of transportation, energy and comunications, the smaller construction tolerances, the bigger power demanded and the media and society awardness to catastrophic events are sufficient motivations for the growing field of structural health monitoring, which aims at assessing the actual condition of a structure and to identify incipient damage. Damage identification can be considered as a two step process, the detection and the diagnosis. The former, and fundamental step, is the confirmation of an efective damage existence. When the response is affirmative, the latter step begins with the diagnosis, and then the questions are: where?, how much?, what type?, when will it fail? In this paper the authors propose a simple method to detect and relatively quantify structural damage by using measured vibrations data, specifically the operational deflections shapes. Numerical simulations and experimental tests are presented to validate the proposed method.

A new damage detection indicator for beams based on mode shape data

In this paper, a new damage indicator based on mode shape data is introduced to identify damage in beam structures. In order to construct the indicator proposed, the mode shape, mode shape slope and mode shape curvature of a beam before and after damage are utilized. Mode shape data of the beam are first obtained here using a finite element modeling and then the slope and curvature of mode shape are evaluated via the central finite difference method. In order to assess the robustness of the proposed indicator, two test examples including a simply supported beam and a two-span beam are considered. Numerical results demonstrate that using the proposed indicator, the location of single and multiple damage cases having different characteristics can be accurately determined. Moreover, the indicator shows a better performance when compared with a well-known indicator found in the literature.

Damage localization of marine risers using time series of vibration signals

Based on dynamic response signals a damage detection algorithm is developed for marine risers. Damage detection methods based on numerous modal properties have encountered issues in the researches in offshore oil community. For example, significant increase in structure mass due to marine plant/animal growth and changes in modal properties by equipment noise are not the result of damage for riser structures. In an attempt to eliminate the need to determine modal parameters, a data-based method is developed. The implementation of the method requires that vibration data are first standardized to remove the influence of different loading conditions and the autoregressive moving average (ARMA) model is used to fit vibration response signals. In addition, a damage feature factor is introduced based on the autoregressive (AR) parameters. After that, the Euclidean distance between ARMA models is subtracted as a damage indicator for damage detection and localization and a top tensioned riser simulation model with different damage scenarios is analyzed using the proposed method with dynamic acceleration responses of a marine riser as sensor data. Finally, the influence of measured noise is analyzed. According to the damage localization results, the proposed method provides accurate damage locations of risers and is robust to overcome noise effect.

A structural damage detection indicator based on principal component analysis and statistical hypothesis testing

A comprehensive statistical analysis is performed for structural health monitoring (SHM). The analysis starts by obtaining the baseline principal component analysis (PCA) model and projections using measurements from the healthy or undamaged structure. PCA is used in this framework as a way to compress and extract information from the sensor-data stored for the structure which summarizes most of the variance in a few (new) variables into the baseline model space. When the structure needs to be inspected, new experiments are performed and they are projected into the baseline PCA model. Each experiment is considered as a random process and, consequently, each projection into the PCA model is treated as a random variable. Then, using a random sample of a limited number of experiments on the healthy structure, it can be inferred using the χ2 test that the population or baseline projection is normally distributed with mean μh and standard deviation σh. The objective is then to analyse whether the distribution of samples that come from the current structure (healthy or not) is related to the healthy one. More precisely, a test for the equality of population means is performed with a random sample, that is, the equality of the sample mean μs and the population mean μh is tested. The results of the test can determine that the hypothesis is rejected (μh ≠ μc and the structure is damaged) or that there is no evidence to suggest that the two means are different, so the structure can be considered as healthy. The results indicate that the test is able to accurately classify random samples as healthy or not.

Damage Detection Using Electromechanical Impedance Technique Combined with Scanning Laser Vibrometry

The paper consists of two parts. First, Electromechanical Impedance (EMI) method is proven to be able to determine some vibrational characteristics of the investigated structure. In order to verify this statement, Scannig Laser Vibrometry (SLV) is used to correlate frequency peaks of velocity (or displacement) operational deflection shapes with corresponding ones obtained by EMI method. Finally, the influence of moisture content in CFRP sample on resonance peaks is investigated using EMI method. Damage detection indicator in this case is based on shifts of resonant peaks.

Prediction of Unmeasured Mode Shape Using Artificial Neural Network for Damage Detection

Artificial neural networks (ANNs) have received much attention in the field of vibration–based damage detection since the 1990s, due to their capability to predict damage from modal data. However, the accuracy of this method is highly dependent on the number of measurement points, especially when the mode shape is used as an indicator for damage detection. With a high number of measurement points, more information can be fed to the ANN to detect damage; therefore, more reliable results can be obtained. Nevertheless, in practice, it is uneconomical to install sensors on every part of a structure; thus the capability of ANNs to detect damage is quite limited. In this study, an ANN is applied to predict the unmeasured mode shape data based on a limited number of measured data. To demonstrate the accuracy of the proposed method, the results are compared with the Cubic Spline interpolation (CS) method. A parametric study is also conducted to investigate the sensitivity of the number of measurement points to the proposed method. The results show that the ANN provides more reliable results compared to the CS method as it is able to predict the magnitude of mode shapes at the unmeasured points with a limited number of measurement points. The application of a two–stage ANN showed results with a high potential for overcoming the issue of using a limited number of sensors in structural health monitoring.

SHM System Based on Modal Filtration

The modal filter has various applications, among the others for damage detection. It was shown, that a structural modification (e.g. drop of stiffness due to a crack) causes an appearance of peaks on the output of the modal filter. This peaks result from not perfect modal filtration due to system local structural changes. That makes it a great indicator for damage detection, which has fallowing advantages: low computational afford due to the data reduction, the structural health monitoring system based on it, is easy to automate. Furthermore the system is theoretically insensitive to environmental changes as temperature or humidity variation (global structural changes do not cause a drop of modal filtration accuracy). In the paper the practical implementation of the presented technique is shown. The developed structural health monitoring (SHM) system is described as well as results of its extensive simulation and laboratory testing. Finally the application of the system for the structural changes detection on the airplane parts is presented..

Sensitivity of PZT Impedance Sensors for Damage Detection of Concrete Structures.

Piezoelectric ceramic Lead Zirconate Titanate (PZT) based electro-mechanicalimpedance (EMI) technique for structural health monitoring (SHM) has been successfullyapplied to various engineering systems. However, fundamental research work on thesensitivity of the PZT impedance sensors for damage detection is still in need. In thetraditional EMI method, the PZT electro-mechanical (EM) admittance (inverse of theimpedance) is used as damage indicator, which is difficult to specify the effect of damage onstructural properties. This paper uses the structural mechanical impedance (SMI) extractedfrom the PZT EM admittance signature as the damage indicator. A comparison study on thesensitivity of the EM admittance and the structural mechanical impedance to the damages ina concrete structure is conducted. Results show that the SMI is more sensitive to the damagethan the EM admittance thus a better indicator for damage detection. Furthermore, this paperproposes a dynamic system consisting of a number of single-degree-of-freedom elementswith mass, spring and damper components to model the SMI. A genetic algorithm isemployed to search for the optimal value of the unknown parameters in the dynamic system.An experiment is carried out on a two-storey concrete frame subjected to base vibrations thatsimulate earthquake. A number of PZT sensors are regularly arrayed and bonded to the framestructure to acquire PZT EM admittance signatures. The relationship between the damageindex and the distance of the PZT sensor from the damage is studied. Consequently, thesensitivity of the PZT sensors is discussed and their sensing region in concrete is derived.

Using the Detection and Relative Damage Quantification Indicator (DRQ) with Transmissibility

The Detection and Relative Damage Quantification Indicator (DRQ) was presented previously as a reliable damage detection indicator when used with Operational Deflection Shapes (ODS). The DRQ was computed from the Response Vector Assurance Criterion (RVAC) between the damaged and the initial ODS and the resulting value proved to be a good indicator of the presence of damage. The use of the ODS implies that the loads applied to the structure with and without damage are either known or, at least, the same. If the forces are not deterministic but still ergodic, the power spectrum could be used to evaluate the ODS, but still the above conditions hold, in a statistical sense. When a structure is subjected to ambient excitation, those conditions can hardly be assured. The loads may vary quite significantly and the ODS changes may be due to those changes instead of the presence of damage. To avoid this handicap, the authors explore here the use of the Transmissibility functions. If properly defined, the Transmissibility is invariant with respect to the amplitude of the loads. Since the Displacement Transmissibility is load invariant, a picked set of responses can be measured in service and used to predict another set; the result will then be correlated to the actual values using the RVAC and the DRQ will be computed. Numerical and experimental examples illustrate the proposed technique.

Damage Detection of a Gear with Initial Pitting Using the Zoomed Phase Map of Continuous Wavelet Transform

Impulsive vibration generated by localized gear damage can be used as an indicator for damage detection. Local damage induces an abrupt increase of the amplitude and phase lag of the impulsive vibration signal measured on the gearbox. Relatively large damage like “tip breakage” can be easily detected by the amplitude map of CWT (continuous wavelet transform) for the impulsive vibration signal measured on the gearbox. However, minor damage like “initial pitting” cannot be detected with the amplitude map. To overcome this problem, in this paper we take into account the phase map for a damage signal. The zoomed phase map of CWT is successfully applied to the detection of minor gearbox damage.

Vibration tests and damage detection of P/C bridges

Abstract A three‐span countryside bridge with simply supported P/C girders has been found to have excessive deflections in two of the three spans. It is reported that the prestresses in tendons of two girders of different spans are lower than design values. This was determined after performing compression tests of concrete core samples, measuring deflections and performing corresponding analysis. This actual bridge provides valuable specimens for studies of damage detection using vibration test data. The objectives of the present study are (a) to prove experimentally that the changes of dynamic characteristics of a bridge can be used as an indicator for damage detection and (b) to study the possibility of application of system identification in defining damage quantitatively. It is found that the changes of mode shapes can be used as an indicator for damage detection. In addition, the use of system identification allows successful quantification of the damaged members in the P/C bridge.