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Abstract: Frequency response function (FRF) estimation is widely used for system identification and related applications, such as model updating and structural health monitoring. Whether estimated from data or from a finite element (FE) model, there is uncertainty involved that introduces variability in the FRF evaluation, which leads to misinterpretation of system identification results or false alarms (Type-I error) if used in hypothesis testing for damage detection. This paper aims to quantify the uncertainty of FRF estimated with randomly-excited experimental data using the H1 estimator, for both its magnitude and phase. A Gaussian bivariate statistical model is implemented, and probability density functions (PDF) of the magnitude and phase estimations are derived. The proposed statistical model is validated for both an ideal simulation test-bed and an experimental lab-scale structure. To further validate the model's robustness, artificial noise is added to contaminate the original data. The predicted PDFs show a very good match with histogram observations, where outlier percentage is selected as the comparison metric in this paper. For different significance levels, the outlier percentages all consistently agree with the pre-described uncertainty thresholds, indicating that the proposed statistical model suitably quantifies the uncertainty of FRF estimations using H1. [Copyright &y& Elsevier]

Model-based damage detection entails the calibration of damage-indicative parameters in a physics-based computer model of an undamaged structural system against measurements collected from its damaged counterpart. The approach relies on the premise that changes identified in the damage-indicative parameters during calibration reveal the structural damage in the system. In model-based damage detection, model calibration has traditionally been treated as a process, solely operating on the model output without incorporating available knowledge regarding the underlying mechanistic behavior of the structural system. In this paper, the authors propose a novel approach for model-based damage detection by implementing the Extended Constitutive Relation Error (ECRE), a method developed for error localization in finite element models. The ECRE method was originally conceived to identify discrepancies between experimental measurements and model predictions for a structure in a given healthy state. Implementing ECRE for damage detection leads to the evaluation of a structure in varying healthy states and determination of discrepancy between model predictions and experiments due to damage. The authors developed an ECRE-based damage detection procedure in which the model error and structural damage are identified in two distinct steps and demonstrate feasibility of the procedure in identifying the presence, location and relative severity of damage on a scaled two-story steel frame for damage scenarios of varying type and severity. [ABSTRACT FROM AUTHOR]

The model-free hysteresis loop analysis (HLA) method for structural health monitoring (SHM) has significant advantages over the traditional model-based SHM methods that require a suitable baseline model to represent the actual system response. This paper provides a unique validation against both an experimental reinforced concrete (RC) building and a calibrated numerical model to delineate the capability of the model-free HLA method and the adaptive least mean squares (LMS) model-based method in detecting, localizing and quantifying damage that may not be visible, observable in overall structural response. Results clearly show the model-free HLA method is capable of adapting to changes in how structures transfer load or demand across structural elements over time and multiple events of different size. However, the adaptive LMS model-based method presented an image of greater spread of lesser damage over time and story when the baseline model is not well defined. Finally, the two algorithms are tested over a simpler hysteretic behaviour typical steel structure to quantify the impact of model mismatch between the baseline model used for identification and the actual response. The overall results highlight the need for model-based methods to have an appropriate model that can capture the observed response, in order to yield accurate results, even in small events where the structure remains linear. [ABSTRACT FROM AUTHOR]

Abstract: The failure of critical components in industrial systems may have negative consequences on the availability, the productivity, the security and the environment. To avoid such situations, the health condition of the physical system, and particularly of its critical components, can be constantly assessed by using the monitoring data to perform on-line system diagnostics and prognostics. The present paper is a contribution on the assessment of the health condition of a computer numerical control (CNC) tool machine and the estimation of its remaining useful life (RUL). The proposed method relies on two main phases: an off-line phase and an on-line phase. During the first phase, the raw data provided by the sensors are processed to extract reliable features. These latter are used as inputs of learning algorithms in order to generate the models that represent the wear''s behavior of the cutting tool. Then, in the second phase, which is an assessment one, the constructed models are exploited to identify the tool''s current health state, predict its RUL and the associated confidence bounds. The proposed method is applied on a benchmark of condition monitoring data gathered during several cuts of a CNC tool. Simulation results are obtained and discussed at the end of the paper. [Copyright &y& Elsevier]

Abstract: Power harvesting techniques that convert vibration energy into electrical energy through piezoelectric transducers show strong potential for powering smart wireless sensor devices in applications of structural health monitoring. This paper presents an analytical model of the dynamic behavior of an electromechanical piezoelectric bimorph cantilever harvester connected with an AC–DC circuit based on the Euler–Bernoulli beam theory and Hamiltonian theorem. A new cantilevered piezoelectric bimorph structure is proposed in which the plug-type connection between support layer and tip-mass ensures that the gravity center of the tip-mass is collinear with the gravity center of the beam so that the brittle fracture of piezoelectric layers can also be avoided while vibrating with large amplitude. The tip-mass is equated by the inertial force and inertial moment acting at the end of the piezoelectric bimorph beam based on D''Alembert''s principle. An AC–DC converting circuit soldered with the piezoelectric elements is also taken into account. A completely new analytic expression of the global behavior of the electromechanical piezoelectric bimorph harvesting system with AC–DC circuit under input base transverse excitation is derived. Moreover, an experimental energy harvester is fabricated and the theoretical analysis and experimental results of the piezoelectric harvester under the input base transverse displacement excitation are validated by using measurements of the absolute tip displacement, electric voltage response, electric current response and electric power harvesting. [Copyright &y& Elsevier]

Abstract: Statistical pattern recognition has recently emerged as a promising set of complementary methods to system identification for automatic structural damage assessment. Its essence is to use well-known concepts in statistics for boundary definition of different pattern classes, such as those for damaged and undamaged structures. In this paper, several statistical pattern recognition algorithms using autoregressive models, including statistical control charts and hypothesis testing, are reviewed as potentially competitive damage detection techniques. To enhance the performance of statistical methods, new feature extraction techniques using model spectra and residual autocorrelation, together with resampling-based threshold construction methods, are proposed. Subsequently, simulated acceleration data from a multi degree-of-freedom system is generated to test and compare the efficiency of the existing and proposed algorithms. Data from laboratory experiments conducted on a truss and a large-scale bridge slab model are then used to further validate the damage detection methods and demonstrate the superior performance of proposed algorithms. [Copyright &y& Elsevier]

Abstract: In this paper a model-based approach to identifying a single crack in a thin, clamped plate undergoing free vibration is described and demonstrated experimentally. Data are gathered from only three resistive strain gages, placed at arbitrary orientations and locations far from the crack. The time series response of the gages to a single impact excitation are then used to estimate the crack parameters that characterize the damage using an efficient finite-element model of the plate. The approach is demonstrated effective in identifying crack location, orientation and length, as well as credible intervals for each. The results show that even with limited, noisy vibration data valuable information regarding the damage state can be successfully estimated. [Copyright &y& Elsevier]

Abstract: System identification in the frequency domain is a very important process in many aspects of engineering. Among many forms of frequency domain system identification such as frequency response function analysis and modal decomposition, transmissibility (output-to-output relationship) estimation has been regarded as one of the most practical tools for its clear physical interpretation, its compatibility with output-only data, and its sensitivity to local changes of structural parameters. Due to operational and environmental variability in any real system, quantization and estimation error, and extraneous measurement noise, the computation of transmissibility may contain a significant level of uncertainty and variability, and these sources propagate to degrade system identification quality and in some cases to system mischaracterization. In this paper, the uncertainty of the magnitude of a transmissibility estimator via output auto-power density spectra is quantified, an exact probability density function for the estimates is derived analytically via a Chi-square bivariate approach, and it is validated with Monte Carlo simulation. Validation shows very consistent results between the observed histogram and predicted distribution for different estimation and noise conditions. [Copyright &y& Elsevier]

Abstract: This paper presents a Bayesian probabilistic methodology to integrate model-based fatigue damage prognosis (FDP) with online and offline structural health monitoring (SHM) data. The prognosis uses fracture mechanics-based fatigue crack growth modeling, along with quantification of various sources of uncertainty, including natural variability, data uncertainty and model errors. These uncertainty sources are connected using a Bayesian network and a probabilistic sensitivity analysis is performed to assess the uncertainty contributions from these sources. The cycle-by-cycle simulation of fatigue crack growth is expedited via the use of a surrogate modeling technique (Gaussian process model) to replace computationally expensive finite element analysis. Real-time monitoring data of external variable amplitude loading history is used to construct a Bayesian autoregressive integrated moving average (ARIMA) model to predict and update the loading. On-ground crack inspection data is used to quantify the uncertainty in the initial and current size of an existing crack, using the Bayesian approach. Three possible cases of inspection results are considered: (1) crack is not detected; (2) crack is detected but not measured; (3) crack is detected and measured. Different scenarios of data availability (load monitoring data and inspection data) are considered for the prognosis of an individual component in a fleet. A numerical example, surface cracking in a rotorcraft mast under service loading, is implemented to illustrate the proposed methodology. The results of prognosis are validated using Bayesian hypothesis testing. [Copyright &y& Elsevier]

Abstract: This paper presents a study for identifying damage locations in an idealized steel bridge girder using the ambient vibration measurements. A sensitive damage feature is proposed in the context of statistical pattern recognition to address the damage detection problem. The study utilizes an experimental program that consists of a two-span continuous steel beam subjected to ambient vibrations. The vibration responses of the beam are measured along its length under simulated ambient vibrations and different healthy/damage conditions of the beam. The ambient vibration is simulated using a hydraulic actuator, and damages are induced by cutting portions of the flange at two locations. Multivariate vector autoregressive models were fitted to the vibration response time histories measured at the multiple sensor locations. A sensitive damage feature is proposed for identifying the damage location by applying Mahalanobis distances to the coefficients of the vector autoregressive models. A linear discriminant criterion was used to evaluate the amount of variations in the damage features obtained for different sensor locations with respect to the healthy condition of the beam. The analyses indicate that the highest variations in the damage features were coincident with the sensors closely located to the damages. The presented method showed a promising sensitivity to identify the damage location even when the induced damage was very small. [Copyright &y& Elsevier]