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The acousto-ultrasonic approach is used for propagating stress waves through different configurations of CORTEN steel specimens. The propagated waves are recorded and analysed by piezoelectric sensors. The purpose of the study is to study the characteristics of the CORTEN steel by analysing the propagated waves.

To investigate the attenuation in acoustic wave propagation due to the corrosion formation in CORTEN steel specimens and to train a neural network model to classify the attenuated acoustic waves automatically.

Due to the corrosion formation in CORTEN steel specimens, attenuation is observed in amplitude, energy, counts and duration of the propagated waves. When the waves are analysed in their time-frequency characteristics, attenuation is observed in their frequency and spectral energy.

The corrosion formation in CORTEN steel can automatically be analysed by using the acousto-ultrasonic approach and the trained deep learning neural network.

The purpose of this paper is to study the feasibility on the use of alternative parameters for representing acoustic emission (AE) and acousto-ultrasonic (AU) signals, using a wavelet-based approach and the computation of Chebyshev moments.

Two tests were performed, one on AE artificial signals generated on a CFRP plate and one on an AU setup used for actively detecting impact damage. The waveforms were represented using a data reduction technique based on the Daubechies wavelet and an image processing technique using Chebyshev moments approximation, to get 32 descriptors for each waveform.

The use of such descriptors allowed in the AE case to verify that the moments are similar when the waveforms are similar; in the AU setup the correlation coefficient of the descriptors with respect to a reference data set was found to be linked to the delimitation size.

Such a data reduction while retaining all the useful information will be positive for wireless sensor networks, where power consumption during data transmission is key. With having to send only a reliable set of descriptors and not an entire waveform, the power consumption is believed to be reduced.

This paper is a preliminary study that fulfils a need for a more reliable data reduction for ultrasonic transient signals, such as those used in AE and AU.

Defects can be caused by a number of factors, such as maintenance damage, ground handling and foreign objects thrown up from runways during an in-service use of composite aerospace structures. Sandwich structures are capable of absorbing large amounts of energy under impact loads, resulting in high structural crashworthiness. This situation is one of the many reasons why sandwich structures are extensively used in many aerospace applications nowadays. Their non-destructive inspection is often more complex. Hence, the choice of a suitable non-destructive testing (NDT) method can play a key role in successful damage detection. The paper aims to discuss these issues.

A comparison of detection capabilities of selected C-scan NDT methods applicable for inspections of sandwich structures was performed using water-squirt, air-coupled and pitch-catch (PC) ultrasonic techniques, supplemented by laser shearography (LS).

Test results showed that the water-squirt and PC techniques are the most suitable methods for core damage evaluation. Meanwhile, the air-coupled method showed lower sensitivity for the detection of several artificial defects and impact damage in honeycomb sandwiches when unfocussed transducers were used. LS can detect most of the defects in the panels, but it has lower sensitivity and resolution for honeycomb core-type sandwiches.

This study quantitatively compared the damage size indication capabilities of sandwich structures by using various NDT techniques. Results of the realised tests can be used for successful selection of a suitable NDT method. Combinations of the presented methods revealed most defects.

In outside constructions (e.g. aircraft frames, bridges, tanks and ships) real‐life noises reduce significantly the capability of location and characterization of crack events. Among the most important types of noise is the rain, producing a signal similar to crack. This paper seeks to present a robust crack detection system with simultaneous raining conditions and additive white‐Gaussian noise at −20 to 20 dB signal‐to‐noise ratio (SNR).

The proposed crack detection system consists of two sequentially, connected modules: the feature extraction module where 15 robust features are derived from the signal and a radial basis function neural network is built up in the pattern classification module to extract the crack events.

The evaluation process is carried out in a database consisting of over 4,000 simulated cracks and drops signals. The analysis showed that the detection accuracy using the most robust 15 features ranges from 77.7 to 93 percent in noise‐free environment. This is a promising method for non‐destructive testing (NDT) by acoustic emission method of aircraft frame structures in extremely noisy conditions.

Continuous monitoring of crack events in the field requires the development of advance noise reduction and signal identification techniques. Robust detection of crack signals in noisy environment, including raining drops, improves significantly the reliability of real‐time monitoring systems in large and complex constructions and in adverse weather conditions.

As far as is known this is the first time that an efficient system is presented and evaluated which deals with the problem of crack detection in adverse environment including both stationary and non‐stationary noise components. Moreover, it provides further information on the engineering and efficiency problems associated with NDT techniques in the aircraft industry.

Presently there exists no way to directly measure strain at high temperatures in engine components such as the combustion chamber, exhaust nozzle, propellant lines, and turbine blades and shaft. The purpose of this paper is to address this issue.

Thermomechanical fatigue (TMF) prediction, which is a critical element for a blade design, is a strong function of the temperature and strain profiles. Major uncertainties arise from the inability of current instrumentation to measure temperature and strain at critical locations. This prevents the structural designer from optimizing the blade design for high temperature environments, which is a significantly challenging problem in engine design.

Being able to directly measure strains in different high temperature zones would deeply enhance the effectiveness of aircraft propulsion systems for fatigue damage assessment and life prediction. The state of the art for harsh environment, high temperature sensors has improved considerably over the past few years.

This paper lays down specifications for high temperature sensors and provides a technological assessment of these new sensing technologies. The paper also reviews recent advances made in harsh environment sensing systems and takes a peek at the future of such technologies.

The purpose of this paper is to present the investigation of damage severity of reinforced concrete (RC) beam subjected to increasing fatigue loading using intensity of acoustic emission (AE) signal.

Together 17 RC beams with dimension of 150 × 150 × 750 mm were prepared. Third point loading fatigue test was performed based on load at the first crack (Pcr) and the ultimate static load (Pult). The frequency of 1 Hz was used with the increasing fatigue loadings, 0.5Pcr (P1), 0.8Pcr (P2), 1.0Pcr (P3), 0.2Pult (P4), 0.5Pult (P5) and 0.6Pult (P6). The damage severity of crack for each phase of loading allowed the identification of the crack modes of the beams, namely, Zone A (no significant emission), Zone B (minor), Zone C (intermediate), Zone D (follow-up) and Zone E (major).

The intensity analysis indicated clear trend with respect to crack propagation in the beam and, hence, can be used to monitor the crack occurrence in the beam.

The intensity analysis has been carried out for the beam subjected to increasing fatigue loading. The analysis was based on the AE data obtained from channel basis and located event.

This paper aims to put forward a nonlinear vibration method for crack detection based on fiber Bragg grating (FBG), which is used to receive the waves in the plate.

First of all, measuring principle of nonlinear vibration technique and FBG sensing principle are introduced. Then nonlinear signal spectrum is analyzed to reveal various models of nonlinear vibration, and modal analysis of the plate structure is performed to lay the foundation for the later experiment. This approach is the cross-modulation effect from a persistent excitation to the receipt signal.

The experimental system is built and its results are in agreement with theoretical analysis, and show that this nonlinear vibration method based on FBG sensor is sensitive to crack damage.

Taking the board structure as the object, a new attempt by nonlinear vibration detection with FBG sensor has been investigated.

A design of sensor networks for structural health monitoring (SHM) with guided waves poses a hard challenge. Therefore different approaches are possible. A known one is the usage of probability of detection (POD) criteria. Here, areas of potential impact sensitivity are calculated for every sensor which leads to a POD. The number of sensors is increased until a demanded POD is reached. However, these calculations are usually based on finite element methods and underlie different assumptions and approximations which can cause different inaccuracies. These limitations are avoided by using an experimental data basis for virtual sensors in this paper. The paper aims to discuss these issues.

An air-coupled ultrasound scanning technique is used for guided wave investigations. Recorded displacements of a structure surface are used as stimulation of virtual sensors which can be designed by software and positioned within available data field. For the calculation of sensor signals an isogeometric finite element model is used. The virtually bonded layer of the virtual piezoceramic sensor interpolates with non-uniform rational B-Splines (NURBS) the measured nodal data for each time step. This interpolation corresponds to a displacement boundary condition and is used to calculate the electrical potential at the free surface of the sensor.

Experimental data based on air-coupled ultrasound scanning technique can be used for elimination of disadvantages in numerical simulations by developing sensor networks for SHM. In combination with a transfer matrix method (TM) a three-dimensional displacement of specimen surface for complex composites can be calculated. To obtain the sensor signal a surface-bonded sensor is modeled by an isogeometric finite element approach. A good accordance is found between calculated virtual sensor signal and its experimental verification.

Some deviations between calculated signal and its experimental verification are mainly justified by different spectral transfer functions between wave field scanning technique and signal recording of applied sensors. Furthermore, sensor influence on wave propagation is neglected in the presented method.

In this paper, the principle of virtual sensors is applied on anisotropic multilayered lamina by using isogeometric finite elements for piezoelectric sensors. This enables any sensor dimension, layout and position on complex composites. Furthermore a bonding layer between specimen and sensor is considered. The method allows a detailed analysis of sensor behavior on a specimen surface and the design and optimization of entire sensor networks for SHM.

Maintenance is one of the most critical and expensive operations during the life cycle of metallic structures, in particular in the aeronautic industry. However, early detection of fatigue cracks is one of the most demanding operations in global maintenance procedures. In this context, non-destructive testing using image techniques may represent one of the best solutions in such situations, especially thermal stress analyses (TSA) using infrared thermography. The purpose of this paper is to access and characterize the main stress profile calculated through temperature variation, for different load frequencies.

In this paper, a cyclic load is applied to an aluminum sample component while infrared thermal image is being acquired. According to the literature and experiments, a cyclic load applied to a material results in cyclic temperature variation.

Frequency has been shown to be an important parameter in TSA evaluations, increasing the measured stress profile amplitude. The loading stimulation frequency and the maximum stress recorded show a good correlation (R² higher than 0.995). It was verified that further tests and modeling should be performed to fully comprehend the influence of load frequency and to create a standard to conduct thermal stress tests.

This work revealed that the current infrared technology is capable of reaching far more detailed thermal and spatial resolution than the one used in the development of TSA models. Thus, for the first time the influence of mechanical load frequency in the thermal profiles of TSA is visible and consequentially the measured mechanical stress.