Damage Detection Capability Research Articles

Structural Properties and Damage Detection Capability of Carbon Nanotube Modified Mortars after Freeze-Thaw.

Here we explore the structural properties and damage sensing of cementitious mortars after a freeze-thaw process (F-T) as a function of nano-modification. For this purpose, carbon nanotubes were added at 0.2–0.8 wt.% cement using two different dispersive agents. F-T resulted in reduced fracture energy in nano-modified specimens prepared using superplasticizer as a dispersant while the opposite held true for the surfactant-containing ones. All nano-modified mortars possessed significantly higher fracture energy compared to the plain specimens after F-T (up to 73% improvement). The acoustic emission activity was lower after F-T, while acoustic emission indicators revealed a more tensile mode of fracture in both plain and nano-modified mortars.

On Transducers Localization in Damage Detection by Wave Propagation Method.

In this paper, the elastic wave propagation method was used in damage detection in thin structures. The effectiveness and accuracy of the system based on the wave propagation phenomenon depend on the number and localization of the sensors. The utilization of the piezoelectric (PZT) transducers makes possible to build a low-cost damage detection system that can be used in structural health monitoring (SHM) of the metallic and composite structures. The different number and localization of transducers were considered in the numerical and experimental analysis of the wave propagation phenomenon. The relation of the sensors configuration and the damage detection capability was demonstrated. The main assumptions and requirements of SHM systems of different levels were discussed with reference to the damage detection expectations. The importance of the damage detection system constituents (sensors number, localization, or damage index) in different levels of analysis was verified and discussed to emphasize that in many practical applications introducing complicated procedures and sophisticated data processing techniques does not lead to improving the damage detection efficiency. Finally, the necessity of the appropriate formulation of SHM system requirements and expectations was underlined to improve the effectiveness of the detection methods in particular levels of analysis and thus to improve the safety of the monitored structures.

Estimation of airframe weight reduction by integration of piezoelectric and guided wave–based structural health monitoring

Current design rules are analyzed and challenged by considering better damage-detection capabilities offered by structure health monitoring systems. The weight-saving potential associated to the integration of such sensing systems is discussed with regards to structural and structure health monitoring system design. Three prospective scenarios are analyzed and considered in a specific use case. According to the most promising scenario (i.e. robust detectability of damages larger than 300 mm2), structural weight reductions of approximately 9% can be achieved. Considering the weight added by the structure health monitoring system, effective weight savings in the order of 5% are achievable. Although this potential is rather modest, applying structure health monitoring systems on structures mainly driven by the damage tolerance criterion is expected to provide reductions far beyond 5%.

Measurement of shallow defects in metal plates using inter-digital transducer-based laser-scanning vibrometer

Scanning laser Doppler vibrometry (SLDV) is a promising method for visualizing defects in plate-like structures. The Lamb wave-based SLDV method using continuous excitations has been recently developed in nondestructive evaluation (NDE). In addition, various signal processing techniques have been developed to enhance damage detection capability for the method. To visualize defects, the A0 mode is commonly used because of its sensitivity to the thickness variations, i.e. the wave speed changes according to the thickness of a plate. The A0 mode is easily generated by lead zirconate titanate (PZT) transducers. However, for thick plates, the A0 mode is found to be less sensitive to thickness variations compared to thin structures. To address this issue, a mode-tuned inter-digital transducer (IDT) is adopted to efficiently generate and utilize the symmetric modes for damage detection and visualization of thick plates. Based on the wavelength of the S0 mode using the dispersion curve of the plate, the IDT is designed and fabricated for efficient symmetric mode generation. To evaluate the capability of the mode-tuned IDT-based SLDV, a 6-mm-thick plate (40 mm × 40 mm) with defects of 0.3 mm, 0.6 mm, 0.9 mm, and 1.2 mm depths is used. The S0 mode is selected for its wavenumber sensitivity and the degree of separation between modes. It is demonstrated that the detectability of shallow defects in a thick plate is improved compared to that when using the A0 mode.

Application of compressive sensing and variance considered machine to condition monitoring

A significant data problem is encountered with condition monitoring because the sensors need to measure vibration data at a continuous and sometimes high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer data than traditional data sampling methods. This sensing paradigm is applied to condition monitoring with an improved machine learning algorithm in this study. For the experiments, a built-in rotating system was used, and all data were compressively sampled to obtain compressed data. The optimal signal features were then selected without the signal reconstruction process. For damage classification, we used the Variance Considered Machine, utilizing only the compressed data. The experimental results show that the proposed compressive sensing method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.

Damage detection of truss structures by hybrid immune system and teaching–learning-based optimization

One of the challenging tasks in structural optimization is locating and evaluating partial damages that have occurred in a system of finite elements. It may be assessed by an inverse problem of assuming stiffness degradation in some elements and comparing the resulting frequencies with those of true/measured values on the damaged structure. The present study formulates this problem as an optimization task and concentrates on teaching–learning-based optimization for its solution. The algorithm is further improved with some concepts of Artificial Immune System to provide better search refinement. Performance of the proposed hybrid method is then evaluated through numerical tests on a number of truss structures as literature benchmarks. The results exhibit superior damage detection capability by the proposed framework, when the vaccination and receptor editing operators are applied within the base algorithm.

Smart cord-rubber composites with integrated sensing capabilities by localised carbon nanotubes using a simple swelling and infusion method

Smart self-sensing composites with integrated damage detection capabilities are of particular interests in various applications ranging from aerospace and automotive structural components, to wearable electronics and healthcare devices. Here, we report a straigthforward methodology to introduce and localise conductive nanofillers into existing elastomeric coatings of reinforcing cords for interfacial damage detection in cord-rubber composites. A simple swelling and infusion method was developed to incorporate carbon nanotubes (CNTs) into the elastomeric adhesive coating of glass cords. Conductive CNT-infused glass cords with good self-sensing functions were achieved without affecting the bonding provided by the coating with the rubber matrix. The effectiveness of using these smart cords as interfacial strain and damage sensors in cord-rubber composites was established under static and cyclic loading and demonstrated its potential in identifying both reversible deformation and irreversible interfacial damage. The simplicity of the proposed swelling and infusion methodology provides great promise for large-scale industrial implementation or the modification of existing elastomeric products.

In Situ Damage Detection for Fiber‐Reinforced Composites Using Integrated Zinc Oxide Nanowires

AbstractA multifunction material that increases strength is capable of harvesting energy from ambient vibration and acts as a structural health monitoring system is presented. Current in situ damage detection of fiber‐reinforced composites typically uses methods which require external sensors, precise initial measurements for each component under evaluation, or input current to the structure. To overcome these limitations, this work utilizes a multifunctional interphase of piezoelectric zinc oxide nanowires integrated into fiber‐reinforced composites to provide an in situ ability to sense damage. The nanowires are grown onto insulating reinforcing fibers which are sandwiched between carbon fiber electrodes, thus fully integrating the sensing element into the fiber‐reinforced composite. The fully distributed nanowire interphase proves capable of detecting multiple damage modes using passive voltage measurements as demonstrated during multiple loading configurations. This work also analyzes voltage emissions corresponding to damage to provide signal characteristics corresponding to the damage state of the specimen to indicate damage progression and the approach of catastrophic failure. The result of this work is thus a multifunctional structural material with damage detection capabilities. The principles investigated in this work can also be extended to alternative structural composites containing integrated piezoelectric materials in the form of nanoparticles, nanowires, or films.

An acousto-ultrasonics approach for probabilistic modelling and inference based on Gaussian processes

In recent years, there has been an increasing interest in employing and analysing complex ultrasonic waves for the purpose of designing health monitoring systems for guaranteeing the safe structural operation by means of early damage prognosis strategies and reducing the costs of maintenance and downtimes. For this reason, monitoring systems are essential and require highly efficient techniques providing a probabilistic interpretation of their diagnostics. On that account, a Bayesian framework within the context of Gaussian processes is adopted in the present work for the purpose of probabilistic data-driven modelling and damage detection. A network of permanently installed piezoelectric sensors in a pitch-catch configuration is used for collecting the propagated ultrasonic waves together with the discrete wavelet transform for feature extraction purposes and matrix unfolding procedures such as the ones used in the monitoring of batch processes for sensor data fusion. The effectiveness of the proposed methodology is experimentally evaluated in three different composite structures. Receiver operating characteristics are used as a statistical measure to assess the damage detection capabilities of the presented method.

Nonlinear Dynamic Modeling of the Cracked Rotor Ball Bearing System With Emphasis on Damage Detection Capabilities

It is confirmed experimentally that in case of a rotor with crack, multiple harmonics are generated when the rotor revolves at a particular frequency. Only few modeling techniques successfully predict this particular behavior of the cracked rotor. It is observed in this research that modeling cracked rotors using conventional finite element methods cannot predict this particular behavior successfully. A nonlinear dynamic model of the flexible rotor with ball bearings is developed using discrete mass spring damper elements combined with an existing model of the crack to truly predict this confirmed experimental behavior. Certain crack detection techniques based on the steady-state response work well on this basic concept of the multiharmonics generation due to nonlinearities caused by cracks in the rotor. The presence of ball bearings, rotor-coupling misalignment, rotor-stator rub, and rotor bow can also cause significant nonlinearities in the overall system. These additional nonlinearities render these crack detection techniques to lose their effectiveness. Our work justifies through simulations that the Jeffcott rotors are the over simplified version of real-life rotor-bearing systems. Hence, these crack detection techniques cannot be efficiently applied for condition monitoring of real-life rotor-bearing systems. The proposed model also helps to understand that the presence of flexible bearing supports affects the dynamics of the system considerably and negatively affects the effectiveness of these crack detection techniques.

The application of compressive sampling in rapid ultrasonic computerized tomography (UCT) technique of steel tube slab (STS).

This paper explores a new method for rapid structural damage inspection of steel tube slab (STS) structures along randomly measured paths based on a combination of compressive sampling (CS) and ultrasonic computerized tomography (UCT). In the measurement stage, using fewer randomly selected paths rather than the whole measurement net is proposed to detect the underlying damage of a concrete-filled steel tube. In the imaging stage, the ℓ1-minimization algorithm is employed to recover the information of the microstructures based on the measurement data related to the internal situation of the STS structure. A numerical concrete tube model, with the various level of damage, was studied to demonstrate the performance of the rapid UCT technique. Real-world concrete-filled steel tubes in the Shenyang Metro stations were detected using the proposed UCT technique in a CS framework. Both the numerical and experimental results show the rapid UCT technique has the capability of damage detection in an STS structure with a high level of accuracy and with fewer required measurements, which is more convenient and efficient than the traditional UCT technique.

A Fiber Bragg Grating-Based Condition Monitoring and Early Damage Detection System for the Structural Safety of Underground Coal Mines Using the Internet of Things

Accurate sensing is the key to structural health monitoring of underground coal mines while using fiber Bragg grating (FBG) sensors. However, the previously developed systems for structural monitoring of underground mines have been limited to monitoring without any capability of damage detection. Therefore, this study integrates a highly accurate FBG monitoring system and output-only data-driven approaches on an Internet of things (IoT)-based platform to develop a comprehensive mine structural safety system. This system relies on a Web 2.0 main server that runs data acquisition, data processing, and damage detection algorithms along with real-time information sharing at remote locations. This system was successfully implemented at the Hassan Kishore coal mine, situated in the Salt Range of Pakistan. Wavelength division multiplexing of the FBG strain sensors reliably captured the effects of dynamic and continuous coal excavation on the stability of mine roadway and access galleries. Principal component analysis, along with hierarchical clustering, was used to determine the damage indicator of the mine. The damage index was validated, showing the minimum value for 2% stiffness reduction. Thus, integration of FBG technology with the Internet can be effectively applied for early safety assessment of underground coal mines and information sharing in real time.

A sensitivity analysis on the damage detection capability of a Lamb waves based SHM system for a composite winglet

Lamb waves based Structural Health Monitoring (SHM) systems, thanks to their high sensitivity to damage detection and the ability to travel over a long distance with low power consumption, are founding increasing industrial applications, especially in the aerospace field, where airworthiness authorities require that composite materials in primary structures must remain undamaged during the in-service life. In order to tolerate damage and monitor its severity and, consequently, to repair the structure only when strictly needed, the use of SHM systems appears to be an efficient solution providing benefits for the current design practice. The continuous assessment of the structural integrity, which can be accomplished by SHM systems, can play a key-role to achieve a less-conservative design as well as to facilitate maintenance operations.This paper deals with a sensitivity analysis, based on the Finite Element (FE) theory, aimed to investigate numerically the influence of the damage orientation on the damage detection capability of a Lamb waves based SHM system arranged on a damaged Glass Fiber Reinforced Polymer composite winglet. Damage detection sensitivity has been measured by analyzing the interaction between the modelled damages and guided waves under a specific central frequency. Signals recorded at different locations by piezoelectric sensors have been compared with the baseline signals achieved under a pristine configuration of the winglet by means of a damage index. A specific trend of the considered damage index has been found out as function of the damage orientation.

Parametric estimation of dispersive viscoelastic layered media with application to structural health monitoring

We present a sequential Bayesian estimation method to estimate the material parameters that govern the one-dimensional propagation of shear waves through continuous, layered, viscoelastic solids. While the proposed estimation method is generic, namely, can be applied to waveform inversion problems that satisfy the above conditions, we here employ it for system identification of building structures. We approximate the linear-elastic response of building structures subjected to low-amplitude earthquake base excitations by a multilayer dispersive shear beam model with Kelvin-Voigt material subjected to vertically propagating shear waves. Utilizing the proposed sequential Bayesian estimation method, we sequentially update the probability distribution function of the unknown parameters to reduce the discrepancies between the estimated and measured frequency response functions. We next verify and validate the performance of the proposed estimation method and investigate the limitations of the presented structural system identification approach using two case studies. In the first case study, we use the simulated structural response of a three-dimensional 52-story building model subjected to bi-directional low-amplitude ground shakings. We estimate the frequency-dependent phase velocity and damping ratio, as well as the mass distribution along the building height. Then, we verify the structural damage detection and localization capabilities of the presented system identification approach by comparing the wave model parameters estimated from simulated response of undamaged and damaged structural models. In the second case study, we use data measured from a shake table experiment on a full-scale five-story reinforced concrete building specimen, where the estimated wave model parameters capture the progressive structural damage in the test specimen. The validation studies suggest that the sequential Bayesian estimation method based on viscoelastic dispersive wave propagation can be used for system and damage identification of building structures.

The wavelet transform as a Gaussian process for damage detection

This paper presents a novel statistical model for the wavelet transform of the acceleration response of a structure based on Gaussian process theory with applications to earthquake damage detection. The proposed model considers the wavelet coefficients at each time sample as a realization of a Gaussian process that depends solely on the damage state of the structure. Damage is then detected by identifying changes in the distribution of the model parameters. The model is purely data driven; it requires no prior knowledge of the structural properties, and all the parameters are learned directly from the measured data. The estimation of the model parameters is transformed to an optimization problem and the convexity of the objective function is investigated. An efficient algorithm for the parameter estimation is proposed and tested for accuracy. Finally, the statistical model is applied to the data obtained from a series of shake table experiments conducted at the University of Nevada, Reno. The results of the application of the proposed statistical model and implementation methodology are presented, and the validity of the model assumptions and damage detection capability are illustrated. A damage detection scheme based on the model parameters and statistical hypothesis testing is proposed and evaluated using the experimental dataset.

Condition monitoring using compressive measurement with variance considered machine algorithm

In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and the improved damage detection capability. A built-in rotating system was used for demonstration. Data were then compressively sampled to obtain compressed measurement. For damage detection, Variance considered machine (VCM) algorithm was employed to classify failure modes of rotating systems. The experimental results showed that the proposed method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.

Efficient swept sine chirp excitation in the non-linear vibro-acoustic wave modulation technique used for damage detection

In this article, the non-linear vibro-acoustic modulation technique is used for structural damage detection. A new experimental configuration and data processing strategy are proposed to improve the damage detection capability of the technique. The swept sine chirp excitation is used for both low-frequency vibration/modal and high-frequency ultrasonic excitations. The adaptive resampling procedure is then applied to extract information about modulation intensity that relates to damage. The proposed method is illustrated using numerical simulations and experimental tests. The latter involves crack detection in an aluminium beam. The results of the proposed method are compared with the classical approach based on single harmonic excitation, demonstrating that similar damage detection information can be extracted. However, the major advantage of the proposed method is simplicity and robustness since no a priori selection of excitation frequencies is needed. As a result, crack detection is more reliable and unambiguous.

Compressive Sensing Approaches for Condition Monitoring and Laser-scanning Based Damage Visualization

Condition monitoring (CM) and Non-destructive testing (NDT) encounter a big data problem because they require to continuously measure vibration or wave data with a high sampling rate. In this study, compressive-sensing approaches for both condition monitoring and non-destructive testing are proposed to efficiently handle a huge amount of data and to improve the damage-detection capability of the current process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer samples compared to traditional sampling methods. For CM experiments, a built-in rotating system was used, and all data were compressively sampled to obtain compressed data. Optimal signal features were then extracted without the reconstruction process and were used to detect and classify damage. Also Non-destructive testing was conducted by using compressed scanning with a Laser Doppler Vibrometer (LDV) system equipped with a mirror tilting device. Wave fields were obtained by scanning through a random and compressive pattern in the spatial domain and full wave fields were reconstructed from the compressively measured data. The damage region was then identified and visualized using wavenumber based signal processing. Experimental results showed that the proposed method could effectively improve the data-processing speed and the detection accuracy of condition monitoring and non-destructive testing.

Air-coupled impact-echo damage detection in reinforced concrete using wavelet transforms

Internal damage detection of reinforced concrete (RC) structures is a challenging field that has garnered increasing attention over the past decades due to a decline in the state of infrastructure in North America. As a nondestructive testing mode, the impact-echo method is currently a promising approach. However, it requires intensive testing to cover large-scale civil RC structures with point-by-point inspection. In order to partially overcome this limitation, this study proposes a new impact-echo analysis method using wavelet transforms with dual microphones with 20 kHz resolution to improve damage detection capability. The signals recorded from the microphones are processed to recover spectral data that are further analyzed using percentage of energy information to determine the condition of the specimen and detect in situ damages. In order to validate the performance of the proposed method, the results from traditional signal processing using FFT and wavelet transforms are compared. The proposed wavelet transform based approach showed better accuracy when covering broader areas, which can contribute to reduce testing time significantly when monitoring large-scale civil RC structures.

압축센싱과 통계학적 기법을 적용한 회전체 시스템의 상태진단

Condition monitoring (CM) encounters a large data problem due to sensors that measure vibration data with a continuous, and sometimes, high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate the efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer samples compared to traditional sampling methods. For the experiments a built-in rotating system was used and all data were compressively sampled to obtain compressed data. Optimal signal features were then selected without the reconstruction process and were used to detect and classify damage. The experimental results show that the proposed method could improve the data processing speed and the accuracy of condition monitoring of rotating systems.