Detection Of Fatigue Damage Research Articles

Feasibility study on time-domain extraction method for zero-frequency pulse signal of nonlinear Lamb waves and early fatigue damage detection

Feasibility study on time-domain extraction method for zero-frequency pulse signal of nonlinear Lamb waves and early fatigue damage detection

Domain generalization-based damage detection of composite structures powered by structural digital twin

This research addresses the challenge of generalizing deep learning models for different CFRP composite structures in the task of fatigue damage detection. To overcome this challenge, knowledge distillation is employed to enhance the generalizability of deep learning models. A teacher network processes continuous wavelet transform images using Fourier transform and neural networks, while a student network distills the teacher network. This framework improves the models' generalization performance by transferring knowledge from the teacher network to the student network. Additionally, soft gradient boosting is utilized to further enhance the generalizability. By constructing a main sub-network and multiple parallel auxiliary sub-networks within the teacher network, the student network mimics the main sub-network to achieve improved accuracy in the target domain and prevent overfitting. To augment limited datasets of real CFRP monitoring signals and help to learn domain-invariant features, structural digital twin technology is leveraged to generate simulated monitoring signals, which enables the models to capture domain invariant information, significantly enhancing its performance of fatigue damage detection across different structures. Damage detection based on the generalization results between multiple Layups demonstrates a test accuracy exceeding 80 % when the monitoring data of the target CFRP structure is unavailable during training. Therefore, the cross-structure damage detection ability of the proposed approach is well proved.

A Review of Non-Destructive Testing for Lithium Batteries

With the rapid development of mobile devices, electronic products, and electric vehicles, lithium batteries have shown great potential for energy storage, attributed to their long endurance and high energy density. In order to ensure the safety of lithium batteries, it is essential to monitor the state of health and state of charge/discharge. There are commonly two methods for measuring lithium batteries: destructive testing and non-destructive testing. Destructive testing is not suitable for in situ or non-destructive analysis as it can cause irreversible deformation or damage to the battery. Herein, this review focuses on three non-destructive testing methods for lithium batteries, including ultrasonic testing, computer tomography, and nuclear magnetic resonance. Ultrasonic testing is widely used in crack and fatigue damage detection. X-ray computer tomography and neutron tomography have gained increasing attention in monitoring the health status of lithium batteries. Nuclear magnetic resonance can be used to conduct in situ and ex situ detection. In this review, non-destructive testing of lithium batteries is summarized, including the current status, achievements, and perspectives of this technology.

Hybrid multi-modal NDE sensing system for in-motion detection and localization of rolling contact fatigue damage in rails

This article presents a multi-modal hybrid-probe approach to nondestructive inspection of RCF cracks and damage in rails. A combination of electromagnetic (EM) and ultrasonic testing (UT) techniques are presented, which allows for complementary physics to be utilized to enhance detection and characterization of surface and sub-surface cracks in a non-contact manner at high speeds. A novel integrated design which combines the motion-induced eddy current (MIEC) effect and ultrasonic Rayleigh surface waves generated and detected using electromagnetic acoustic transducer (EMAT) is presented. The hybrid probe was tested at low speeds to demonstrate an increased damage localization capability. This was carried out using a data registration and fusion approach between the sensing modalities. Finally, the capability of MIEC effect at high-speeds is demonstrated. The results show that the hybrid probe has a high potential for in-motion, high-speed damage detection and characterization in the future.

On the random vibration-based progressive fatigue damage detection and classification for thermoplastic coupons under population and operational uncertainty

The problem of vibration-based progressive fatigue damage detection and Fatigue State (FS) determination for thermoplastic coupons is experimentally addressed under significant population and experimental uncertainty. The study involves 13 coupons subjected to tension–tension fatigue testing with interruptions at 10,000-cycle intervals. Utilizing non-parametric random vibration analysis and a robust, uncertainty-tolerant methodology, the research employs Multiple Model Representations of uncertain dynamics through parametric ARMA(AutoRegressive-Moving Average)-type random vibration response modeling. Results, validated by ultrasonic C-Scan testing, reveal (a) the significance of uncertainties, (b) a resonant structural frequency indicating fatigue accumulation, (c) remarkable detection performance, achieving 100 % accuracy even at 10,000 cycles, and (d) FS determination with over 80 % accuracy across all coupons. This study offers promising avenues for automated fatigue damage detection and FS determination in thermoplastic structures, eliminating the need to interrupt their operational cycles.

The Detection of Local Impact Fatigue Damage on Metal Materials by Combining Nonlinear Acoustic Modulation and Coda Wave Interferometry

The Detection of Local Impact Fatigue Damage on Metal Materials by Combining Nonlinear Acoustic Modulation and Coda Wave Interferometry

Feasibility study of nonlinear ultrasonic imaging using a contact pulse-echo method for fatigue damage evaluation

ABSTRACT A contact pulse-echo nonlinear imaging method is proposed for the detection and localisation of fatigue damage in solid materials. A dual element transducer is designed based on nonlinear wave simulations and manufactured to improve the signal-to-noise ratio of harmonic waves. The transducer is clamped on a fixture device for scanning. The proposed method is introduced for scanning and imaging stainless steel specimens with different fatigue damage states. Experimental results for the specimen in the initial state show that both the fundamental and second harmonic waves are well measured and the imaging of nonlinear parameters is very stable over the scanning area. The imaging results for the specimen with an initial fatigue crack show that the fundamental waves remain the same but the second harmonic waves vary significantly in the potential growth area of the fatigue crack. Furthermore, the imaging results for the seriously fatigued specimen show that the amplitudes of fundamental waves decrease near the fatigue crack, while the amplitudes of second harmonic waves increase. These results indicate that the proposed pulse-echo method is effective for nonlinear ultrasonic imaging and early damage evaluation. The factors affecting the nonlinear waves in the lab experiments and possible problems with practical applications are also discussed for the proposed method.

Quantitative characterization of fatigue damage in plate structures based on FSOM

For the problem of fatigue damage detection and damage degree assessment of plate structures, a quantitative damage assessment method based on the fast self-organizing feature mapping (FSOM) algorithm is proposed in this paper. The damage detection problem is transformed into a binary classification problem by extracting multidimensional damage features of the Lamb wave signal in plate to be detected and selecting damage sensitive features. Then, the FSOM network is used to identify the health state of the plate to be inspected, and the damage index is obtained by fusing the damage sensitive features using FSOM to quantitatively evaluate the damage level of the plate to be inspected. Simulation and experimental results show this method has a good dynamic tracking capability for the fatigue damage evolution of aluminum and composite plates, and can achieve quantitative assessment of fatigue damage of plate structures.

Detection of early fatigue damage during ultrasonic fatigue testing of steel by acoustic emission monitoring

Ultrasonic fatigue testing (USFT) is a time-efficient method for evaluation of the fatigue limit of metallic alloys in a high and very high cycle range. Propagation of a final crack at the very end of the fatigue life can be clearly detected by USFT parameters and monitoring techniques. In contrast, initiation of fatigue damage during USFT remains unclear. Despite its excellent sensitivity, the implementation of acoustic emission (AE) for damage monitoring is hindered by severe noisiness of acoustic signals from USFT. In addition to resonance-related quasi-stationary noises, USFT in a pulse-pause mode is accompanied by non-stationary ones, which makes recognition of material-related AE signals even more difficult. A special AE processing algorithm was developed to overcome this issue and get useful insights from USFT monitoring. Consistent cropping, Fourier transformation, adaptive filtration and thresholding allowed to calculate noise-free AE activity during USFT of 42CrMo4 steel. Remarkably, most of this activity was located in the very beginning of fatigue loading. In comparison to non-failed runout samples, AE activity of samples with fatigue cracks was significantly higher, indicating its relation to fatigue damage. The proposed AE processing principle can be helpful for monitoring of USFT samples and other parts, operating in resonance conditions.

Evolving fatigue damage detection based on multi-component nonlinear modulation mechanism of tuned laser-generated Rayleigh waves

In this paper, we clarify the nonlinear interaction between tuned Rayleigh waves and evolving fatigue damage and achieve evolving fatigue damage detection. Firstly, the impact of laser parameters on Rayleigh waves is investigated to generate stronger Rayleigh waves. Secondly, the multi-component nonlinear modulation mechanism of Rayleigh waves is analyzed using the nonlinear model considering contact and friction. The simulation results indicate that during the plastic deformation stage, waveform distortion is minimal. As microcracks form, the longitudinal wave component becomes dominant in Rayleigh wave nonlinearity due to contact and friction behaviors between microcrack faces, resulting in waveform distortion, harmonic generation, and increased acoustic nonlinearity parameters. However, the shear wave component only exhibits high-frequency filterability without nonlinearity. As macrocracks develop, both longitudinal and shear wave component amplitudes decrease significantly. Finally, an experimental validation is carried out. The experimental results validate that the acoustic nonlinearity parameters first stabilize and then increase significantly from the pristine state to the microcrack propagation, and the indicator can be utilized for fatigue damage detection. The research can provide guidance for nonlinear laser-generated Rayleigh wave detection of fatigue damage.

Nonlinear frequency response function: experimental study on gearbox fault detection under step load

For rotating machinery, excitation signals are difficult to measure, making it impossible to detect fatigue damage using nonlinear frequency response function. The purpose of this study is to propose a fatigue damage detection method for gearboxes under stepped load conditions based on normalized nonlinear output frequency response functions (NOFRFs). That is, using the vibration signals from the gearbox during the step loading to calculate the cross power spectrum, and therefrom estimating the normalized NOFRFs. This method is used to detect gearboxes containing prefabricated damaged gears or bearings. The research result shows that gear and bearing damage can lead to an increase in the index value of normalized NOFRFs. The different type of damage lead to the significant diversification of higher-order normalized NOFRFs, which further demonstrates that the method proposed in this article is not only effective in gearbox damage detection, but also has certain potential in fault classification. Then, experimental research for accumulated fatigue damage of gear was conducted. The results show that the normalized NOFRFs index is a better indicator of fatigue damage than the kurtosis index.

Random vibration-based progressive fatigue damage monitoring in thermoplastic coupons: a preliminary investigation

In recent years there has been an increasing trend towards the utilization of composites, particularly thermoplastics, in various components of aerostructures. The prelusion of such materials has underscored the significance of investigating their fatigue behavior and developing reliable methods for detecting fatigue damage. In this context, vibration-based techniques hold significant potential as they leverage the inherent excitation provided by in-flight noise and turbulence. This study aims at assessing the progressively accumulated fatigue damage in thermoplastic coupons via random vibration signals while accounting for operational and inter-structural uncertainties. The experimental process consists of preliminary tension and fatigue tests, interrupted fatigue tests, C-Scan inspection tests, and non-contact random vibration tests. Consecutive fatigue states are obtained by performing fatigue tests at intervals of 10 000 cycles for a population of 7 coupons. At each interruption, ultrasonic C-Scan and vibration inspection tests are performed, allowing for the visualization of fatigue damage and random vibration signal analysis. Welch Power Spectral Density estimates are employed and are shown to have good potential for distinguishing among different fatigue states despite the inevitable population and experimental uncertainty. Furthermore, fatigue damage is found to progress symmetrically and laterally along the free edges of the test coupons, which is explained by the free edge effect.

An estimation method of normalized nonlinear output frequency response functions for fatigue damage detection using stochastic response signal

AbstractNonlinear output frequency response functions (NOFRFs) and its modified version, normalized NOFRFs (norm‐NOFRFs), are composed of multiple spectra, which can sensitively indicate the occurrence of fatigue damage in machinery. However, their application is restricted to a certain extent. Currently, neither NOFRFs nor norm‐NOFRFs can be estimated with only a single response signal. Aimed at this problem, the nonlinear auto‐regressive (NAR) model was introduced, and an estimation method of norm‐NOFRFs is proposed in this paper. Only one stochastic response signal is required in the proposed method, which removes the restrictions on the number and intensity of signals to identify norm‐NOFRFs, and expands the range of scenarios for using norm‐NOFRFs in damage detection. The feasibility and robustness of the proposed method are verified by the nonlinear model in numerical simulation. The effectiveness of revealing and quantifying the fatigue damage is verified by experimental study for three kinds of mechanical parts.

Fatigue damage detection method of nano ceramic coating rail based on acoustic signal acquisition

Fatigue damage detection method of nano ceramic coating rail based on acoustic signal acquisition

Fatigue damage detection method of nano ceramic coating rail based on acoustic signal acquisition

Fatigue damage detection method of nano ceramic coating rail based on acoustic signal acquisition

Comprehensive surface integrity evaluation during early stages of gear contact fatigue

The study aims to evaluate the microstructural sensitive aspects of contact fatigue crack initiation and its evolution during the gear lifetime. Tests were performed to evaluate different stages in the evolution curve of gear contact fatigue. The magnetic Barkhausen noise (MBN) technique was used to characterize variations in the magnetic response in gears during the incipient gear contact fatigue mechanisms initiation. For a complete comprehension of the surface degradation, residual stresses, microstructure, and microhardness were explored. A substantial increase in the MBN signal is identified before the fatigue failure occurs, indicating the occurrence of microstructural alterations that change the magnetic properties. The early stages of contact fatigue are accompanied by a surface softening in the near-surface region, up to approximately 40 µm depth. These phenomena were also followed by a less compressive residual stress region at 20 µm depth. A lower influence of microstrains on the diffractogram can be observed by the full width at half maximum parameter (FWHM), indicating a higher amount of dislocation annihilation during the appearance of the initial stages of the contact fatigue mechanism. The study concludes by proposing a comprehensive approach to understand the mechanisms behind the early stages of gear contact fatigue and how the MBN signal can be used to detect fatigue damage.

Application of the acoustic emission method for ranking by fatigue damage of the material of trunnions of drying cylinders of cardboard and paper-making machines

Using the example of assessing the technical condition of the trunnions of drying cylinders of cardboard-making machines (CDM), the question of the possibility of ranking cyclically loaded elements of dynamic equipment according to the degree of damage to their material by fatigue cracks using acoustic emission (AE) measurements is discussed. As a result of special laboratory studies with varying loads in the cycle of loading and lubrication of the crack banks, the features of AE from the friction of the crack banks and plastic deformation at the crack tip during its growth in a viscous material have been established. It is shown that during cyclic loading of the material, AE signals from the friction of its shores are detected more steadily than AE signals from a crack jump with an increase in its length, and that tracking the former ensures the detection of fatigue damage of the material even under loading conditions insufficient for crack growth. Based on the data obtained, three AE signs of the presence of fatigue damage of the material of the elements of dynamic equipment were developed under the condition of cyclic activation of the movement of the crack banks. As a result of industrial testing of the developed AE features, their operability was confirmed and their boundary values on the operating trunnions of CDM drying cylinders were clarified, and a method for ranking trunnions by the level of damage by fatigue cracks was proposed. By comparing the results of AE measurements with ultrasound control, the reliability of the proposed approach was evaluated, which showed the probability of detecting a fatigue crack in the trunnions of CDM drying cylinders at the level of 71 %, with the probability of their skipping and false rejection of the product 12 and 17 %, respectively. The developed technique is also transferable to other massive dynamic equipment after clarifying the boundary values of AE signs of fatigue damage of the material on this type of object.

A random forest‐based fatigue damage classification framework using global loading and displacement information

AbstractThe article presents a random forest‐based damage classification methodology built upon the force and displacement data obtained via fatigue testing of Al7075‐T6 specimens. Four features, namely, average displacement (D), material stiffness (ST), energy dissipation rate (EDR), and cumulative energy dissipation (CED), are defined from the collected data. These features are classified into healthy and cracked states based on the information obtained from a high‐resolution confocal microscope and a digital microscope. Random forest‐based classifiers are, thereafter, trained and tested using the individual as well as different combinations of the four features. The results reveal that, when used individually, CED shows the best accuracy of 80% in detecting a fatigue crack. When two or more pairs of features are used, the accuracy increases significantly to beyond 95%. CED is observed to be the most important feature for developing efficient classifiers for fatigue crack detection. The results demonstrate that the random forest classifier is a viable option to accurately detect fatigue damage based on the four features using only global information.

Towards numerical simulation of fatigue damage detection in CFRP laminates by modelling of stochastic vibration response

Due to the widespread use of composites in aerospace applications, there is an increasing need for developing Structural Health Monitoring (SHM) systems for composite structures. In this process, simulation models can play a crucial role by contributing to the optimization of the experimental techniques and to understanding of the physical mechanisms. The objective of this work is to numerically simulate fatigue damage detection in laminated composites subjected to random vibration through a white gaussian noise. To this end, the ANSYS FE code has been used. The developed FE model represents an experimental set-up consisting of the composite specimen, the speakers producing the white noise and the measuring equipment. 24 specimens were modelled in total, a healthy one and 23 damaged ones. The white noise was modelled by introducing a normalized sequence of numbers adapted to the free vibration eigenfrequency range. Fatigue damage in the form of matrix cracking and delamination was modelled based on C-Scan images of fatigued specimens by degrading the appropriate elastic properties of the layers in specific groups of elements. Damage of different geometry, size, and type was modelled. The numerical results are in the form Power Spectral Density (PSD) diagrams, natural frequencies, eigenfrequencies and contour plots. The comparison between the numerical and experimental eigenfrequency values show a maximum deviation of 1.7%. The numerical results show a change in the vibration response even in the presence of a small damage, which is an indication of the sensitivity of the method. A significant decrease in natural frequencies of the specimen with the damage accumulation is observed. The present model comprises the first effort towards a fully validated digital twin which will be used for the virtual testing and the optimization of the vibration test.

Development and Validation of a Low-Cost Device for Real-Time Detection of Fatigue Damage of Structures Subjected to Vibrations.

This paper presents the development and validation of a low-cost device for real-time detection of fatigue damage of structures subjected to vibrations. The device consists of an hardware and signal processing algorithm to detect and monitor variations in the structural response due to damage accumulation. The effectiveness of the device is demonstrated through experimental validation on a simple Y-shaped specimen subjected to fatigue loading. The results show that the device can accurately detect structural damage and provide real-time feedback on the health status of the structure. The low-cost and easy-to-implement nature of the device makes it promising for use in structural health monitoring applications in various industrial sectors.