The Digital Twin concept represents an innovative method to monitor and predict the performance of an aircraft’s various subsystems. By creating ultra-realistic multi-physical computational models associated with each unique aircraft and combining them with known flight histories, operators could benefit from a real-time understanding of the vehicle’s current capabilities. One important facet of the Digital Twin program is the detection and monitoring of structural damage. Recently, a method to detect fatigue cracks using the transformation response of shape memory alloy (SMA) particles embedded in the aircraft structure has been proposed. By detecting changes in the mechanical and/or electromagnetic responses of embedded particles, operators could detect the onset of fatigue cracks in the vicinity of these particles. In this work, the development of a finite element model of an aircraft wing containing embedded SMA particles in key regions will be discussed. In particular, this model will feature a technique known as substructure analysis, which retains degrees of freedom at specified points key to scale transitions, greatly reducing computational cost. By using this technique to model an aircraft wing subjected to loading experienced during flight, we can simulate the response of these localized particles while also reducing computation time. This new model serves to demonstrate key aspects of this detection technique. Future work, including the determination of the material properties associated with these particles as well as exploring the positioning of these particles for optimal crack detection, is also discussed.

In this work, the effect of a crack on the vibrational properties of a shaft-disc system has been studied applying a generalized harmonic balance method. In the reviewed literature, the reported methods to find the unbalance response of a continuous shaft-disc system provide only the first harmonic component of the response; whereas, the presented method gives the super-harmonic components as well. The shaft-disk system consists of a flexible shaft with a single rigid disc mounted on rigid short bearing supports. The shaft contains a transverse breathing crack (fatigue crack). The main concept for crack detection in vibration-based methods is basically the investigation of crack-induced changes in the selected vibrational properties. Shaft critical speeds and harmonic and super-harmonic components of the unbalance lateral response have been used as typical vibrational properties for crack detection in a rotating shaft system. A generalized harmonic balance method has been developed to efficiently investigate changes in vibrational properties due to the effect of crack properties, depth and location. The results of the developed analytical model have been compared with those obtained from the finite element model and close agreement has been observed.

Fatigue cracks often occur in structural components due to dynamic loadings acting on them, such as wind loads and ground motion. If the static deflection due to dead loads is smaller than the vibration amplitude caused by dynamic loadings, these fatigue cracks alternately open and close with time, exhibiting a breathing-like behavior. This type of crack leads to a smaller change in structural dynamic characteristics than open cracks, and thus it is more difficult to be detected. If undetected timely, these fatigue cracks may lead to a catastrophic failure of the overall structure. Considering that breathing cracks introduce bilinearity into the structure, the present authors first developed a simple and efficient system identification method for bilinear systems by separating the global responses into two parts and performing Fourier transform on each set of separated data [1]. By applying this method, the natural frequency of each stiffness region can be identified. Then, breathing fatigue cracks can be detected by looking for the difference in the identified natural frequency between stiffness regions [1]. That approach is only applicable to the cases where the intact structure is linear. This study is to extend the approach in [1] to the cases when the intact structure is nonlinear, e.g., a structure with large displacements (geometrical nonlinearity). Once breathing cracks occur, there will exist both bilinearity (caused by breathing cracks) and cubic nonlinearity (caused by large displacements). To detect fatigue cracks in this case, Hilbert transform is proposed to be employed to process the separated data, instead of employing Fourier transform as in [1]. This approach has been successfully validated by numerical simulations.

A method for detecting fatigue cracks has been explored at NASA Langley Research Center. Microscopic NiTi shape memory alloy (sensory) particles were embedded in a 7050 aluminum alloy matrix to detect the presence of fatigue cracks. Cracks exhibit an elevated stress field near their tip inducing a martensitic phase transformation in nearby sensory particles. Detectable levels of acoustic energy are emitted upon particle phase transformation such that the existence and location of fatigue cracks can be detected. To test this concept, a fatigue crack was grown in a mode-I single-edge notch fatigue crack growth specimen containing sensory particles. As the crack approached the sensory particles, measurements of particle strain, matrix-particle debonding, and phase transformation behavior of the sensory particles were performed. Full-field deformation measurements were performed using a novel multi-scale optical 3D digital image correlation (DIC) system. This information will be used in a finite element-based study to determine optimal sensory material behavior and density.

Monitoring of fatigue cracking in steel bridge structures using a combined passive and active scheme has been approached by the authors. Passive acoustic emission (AE) monitoring is able to detect crack growth behavior by picking up the stress waves resulting from the breathing of cracks while active ultrasonic pulsing can quantitatively assess structural defect by sensing out an interrogating pulse and receiving the structural reflections. The dual-mode sensing functionality is pursued by using the R15I ultrasonic transducers. In the paper, we presented the subject dual-mode sensing on steel compact tension (CT) specimens in a laboratory setup. Passive AE sensing was performed during fatigue loading and showed its capability to detect crack growth and location. At selected intervals of loading cycles, the test was paused to allow for active sensing by pulsing the transducers in a round-robin pattern. Plate waves were excited, propagated and interacted within the structure. Several approaches were proposed to analyze the interrogation data and to correlate the data features with crack growth. Root means square deviation (RMSD) damage index (DI) was found as a good indicator for indicating the overall crack development. Short time Fourier transform (STFT) provided both time and frequency information at the same time. Moreover, wave velocity analysis showed interesting results when crack developed across the transmitter-receiver path.

The work presented in this paper provides an insight into the current challenges to detect incipient damage in complex metallic structural components. The goal of this research is to improve the confidence level in diagnosis and damage localization technologies by developing a robust structural health management (SHM) framework. Improved methodologies are developed for reference-free localization of fatigue induced cracks in complex metallic structures. The methodologies for damage interrogation involve damage feature extraction using advanced signal processing tools and a probabilistic approach for damage detection and localization. Specifically, piezoelectric transducers are used in pitch-catch mode to interrogate the structure with guided Lamb waves. A novel time-frequency (TF) based signal processing technique based on the matching pursuit decomposition (MPD) algorithm is developed to extract time-of-flight damage features from dispersive guided wave sensor signals, followed by a Bayesian probabilistic approach used to optimally fuse multi-sensor information and localize the crack tip. The MPD algorithm decomposes a signal using localized TF atoms and can provide a highly concentrated TF representation. The Bayesian probabilistic framework enables the effective quantification and management of uncertainty. Experiments are conducted to validate the proposed detection and localization methods. Results presented will illustrate the usefulness of the developed approaches in detection and localization of damage in aluminum lug joints.

Many structural damage detection methods utilize piezoelectric sensors. While these sensors are efficient in supporting many structural health monitoring (SHM) methodologies, there are a few key disadvantages limiting their use. The disadvantages include the brittle nature of piezoceramics and their dependence of diagnostic results on the quality of the adhesive used in bonding the sensors. One viable alternative is the utilization of Magneto-Elastic Active Sensors (MEAS). Instead of mechanically creating elastic waves, MEAS induce eddy currents in the host structure which, along with an applied magnetic field, generate mechanical waves via the Lorentz force interaction. Since elastic waves are generated electromagnetically, MEAS do not require direct bonding to the host structure and its elements are not as fragile as PWAS. This work explores the capability of MEAS to detect damage in aluminum alloy. In particular, methodologies of detecting fatigue cracks in thin plates were explored. Specimens consisted of two identical aluminum plates featuring a machined slot to create a stress riser for crack formation. One specimen was subjected to cyclic fatigue load. MEAS were used to transmit elastic waves of different characteristics in order to explore several SHM methodologies. Experiments have shown that the introduction of fatigue cracks created measurable amplitude changes in the waves passing through the fatigued region of the aluminum plate. The phase indicated sensitivity to load conditions, but manifestation in the cracked region lacked stability. Nonlinear effects were studied using plate thickness resonance, which revealed birefringence due to local stresses at the site of the fatigue crack. The resonance spectrum has also shown a frequency decrease apparently due to stiffness loss. Preliminary results suggest opportunities for fatigue damage detection using MEAS. Application of MEAS for the diagnosis of complex structures is currently being investigated.

The use of aluminum alloys in the design of naval structures offers the benefit of light-weight ships that can travel at high-speed. However, the use of aluminum poses a number of challenges for the naval engineering community including higher incidence of fatigue-related cracks. Early detection of fatigue induced cracks enhances maintenance of the ships and is critical for preventing the catastrophic failure of the hull. Furthermore, monitoring the integrity of the aluminum hull can provide valuable information for estimating the residual life of hull components. This paper presents a model-based damage detection methodology for fatigue assessment of hulls that are instrumented with a long-term hull monitoring system. At the core of the data driven damage detection approach is a Bayesian model updating algorithm enhanced with systematic enumeration and pruning of candidate solutions. The Bayesian model updating approach significantly reduce the computational effort by systematically narrowing the search space using errors functions constructed using the estimated modal properties associated with the condition of the structure. This study proposes the use of the Bayesian model updating technique to detect damage in an aluminum panel modeled using high-fidelity finite element models. The performance of the proposed damage detection method is tested through simulation of a progressively growing fatigue crack introduced in the vicinity of a welded stiffener element. An experimental study verifies the accuracy of the proposed damage detection method using an aluminum plate excited with a controlled excitation in the laboratory.

Fatigue is one of the most widespread damage mechanisms found in metallic structures. Fatigue is an accumulated degradation process that occurs under cyclic loading, eventually inducing cracking at stress concentration points. Fatigue-related cracking in operating structures is closely related with statistical loading characteristics, such as the number of load cycles, cycle amplitudes and means. With fatigue cracking a prevalent failure mechanism of many engineered structures including ships, bridges and machines, among others, a reliable method of fatigue life estimation is direly needed for future structural health monitoring systems. In this study, a strategy for fatigue life estimation by a wireless sensor network installed in a structure for autonomous health monitoring is proposed. Specifically, the computational resources available at the sensor node are leveraged to compress raw strain time histories of a structure into a more meaningful and compressed form. Simultaneous strain sensing and on-board rainflow counting are conducted at individual wireless sensors with fatigue life prediction made using extracted amplitudes and means. These parameters are continuously updated during long-term monitoring of the structure. Histograms of strain amplitudes and means stored in the wireless sensor represent a highly compressed form of the original raw data. Communication of the histogram only needs to be done by request, dramatically reducing power consumption in the wireless sensing network. Experimental tests with aluminum specimens in the laboratory are executed for verification of the proposed damage detection strategy.

This paper presents a microwave antenna sensor that has the ability to monitor fatigue cracks between lap joints where compressive force is present or in any other areas where a crack is hidden between two parts of a structure. The sensor is designed to be low profile, light weight, and conformal, making it ideal for aerospace applications where lap joints are a common design feature, and thus minimizing manual inspection times. The design of the fatigue sample for simulating a lap joint will be presented. The experiment results validated the microwave antenna sensor’s capability to effectively monitor fatigue cracks under lap joints.

This paper examines the vibration-based monitoring technique to quantify the smallest crack size that is detectable in Aluminum beams using piezoceramic excitation and sensing. Having the analytical model of the effect of crack formation on the frequency response of the system, the effect of temperature is also taken into consideration to have a better understanding of the damage effect. The analytical model used in the present work is based on the recent model introduced by Aydin (2008) which is a simplified version of the model used by Khiem et. al. (2001). The beam studied here is assumed to be a uniform Euler-Bernoulli beam having a single fatigue crack and being axially loaded. The crack is treated as a localized reduction in the stiffness and is modeled as a massless rotational spring at the location of the crack, connecting the bisections of the beam. The beam is assumed to be simply supported and subject to a uniform heat flux along the top surface of the beam. For the simplicity in the modeling, it is assumed that the bottom surface of the beam is insulated. The crack is also assumed to be non-breathing during the deformation of the beam. The change in the temperature will alter the modulus of elasticity of the beam and will also cause thermal moments inside the beam which will add terms in both the equation of motion and the boundary conditions of the vibrating beam. First, the effect of temperature on the modulus of elasticity of the beam is studied analytically for different boundary conditions of the beam ends. These modeling results are then compared to the experimental ones. Second, the effect of temperature variation is analytically modeled into the equation of motion of the beam and the boundary conditions. Having the equation of motion of the vibrating beam, the effect of temperature on the frequency response of the beam having a single fatigue crack is studied. Taking into account the effect of temperature on the resonance frequency of the beam will be essential in distinguishing the two effects of damage presence and temperature variation and will be important in quantifying the smallest detectable crack in a structure.

This paper looks at the impedance-based and vibration methods used for the structural health monitoring (SHM) of aluminum beams and attempts to quantify the smallest fatigue crack size that is detectable by these two methods. The vibration-based method presented in this paper, uses the recent model of Aydin [1] which is based on a simple Euler-Bernoulli beam model. This method treats cracks as localized reduction in the beam’s stiffness and models them as massless rotational springs at the locations of the cracks. The beam is then considered to be of multiple sections connected by these springs. The beam studied in the present work is assumed to be an aluminum, uniform, Euler-Bernoulli beam having a single fatigue crack and being axially loaded. It is further assumed that frequencies can only be measured to within half a Hertz. This results in formulas that can be used to predict specific detectable sizes of fatigue cracks given specific geometry of the beam. For example for a beam of dimension 240×19.1×4.8 mm, it is found that the fatigue crack must be approximately 12.5% of the beam width in order to induce a frequency shift of 0.5 Hz. In the second part of this paper, different sets of experiments are conducted on aluminum beams. First, saw-cuts are made in the beams and the resultant shift in the beams’ natural frequency is examined to find the minimum detectable cut length. In order to improve this minimum detectable damage size, the beat frequency method is applied, which enhances the minimum detectable frequency shift. These results are then compared to those of the electrical impedance measurements through the HP 4194A Impedance analyzer. At the end, the aluminum beams are being fatigued and by measuring their electrical impedance at different numbers of fatigue cycling, their detectable fatigue crack size is investigated.

The use of the posterior Crame´r-Rao lower bound (PCRLB) as a lower bound for the mean-squared estimation error (MSEE) of progressive damage is investigated. The estimation problem is formulated in terms of a stochastic dynamic system model that describes the random evolution of damage and provides measurement uncertainty. Based on whether the system is linear or nonlinear, sequential Monte Carlo techniques are used to approximate the posterior probability density function and thus obtain the damage state estimate. The resulting MSEE is compared to the lower bound offered by the PCRLB that is obtained from the implied state transition probability density function and the measurement likelihood function. The progressive estimation results and the PCRLB are demonstrated for fatigue crack estimation in an aluminum compact-tension (CT) sample subjected to variable-amplitude loading.

The primary focus of this paper is to report on the technique developed to extend a simulated damage site (such as a delamination) without inducing other extraneous damage modes. This was done in order to assess the suitability of curvature mode shape analyses in detecting damage types which are similar in type but different in severity or size. This paper highlights the use of vibration based testing on Carbon/Epoxy composite beams for damage detection. Such composites are commonly used in the aerospace and marine industry. The study comprises of testing carbon/epoxy composite beams with various embedded delaminations with a mechanical actuator and a Scanning Laser Vibrometer (SLV) as a sensor for recording the frequency response and the subsequent analyses of the acquired dynamic response based on Displacement and Curvature Mode Shapes. The paper also discusses the Finite Element Method (FEM)-based Analysis to validate the experimental results. In order to assess the effect of an increasing damage zone on a particular damage configuration, it was necessary to extend the damage without inflicting other damage types in the process. This paper reports on an innovative way of extending an existing delamination by a fatigue crack-growth technique. The ASTM E399-90 standard was used for the experiment and a carefully designed fatigue crack growth routine was implemented to advance the delamination in a controlled manner.