Damage Severity Assessment of Multi-Layer Complex Structures Based on a Damage Information Extraction Method with Ladder Feature Mining.

Multi-layer complex structures are widely used in large-scale engineering structures because of their diverse combinations of properties and excellent overall performance. However, multi-layer complex structures are prone to interlaminar debonding damage during use. Therefore, it is necessary to monitor debonding damage in engineering applications to determine structural integrity. In this paper, a damage information extraction method with ladder feature mining for Lamb waves is proposed. The method is able to optimize and screen effective damage information through ladder-type damage extraction. It is suitable for evaluating the severity of debonding damage in aluminum-foamed silicone rubber, a novel multi-layer complex structure. The proposed method contains ladder feature mining stages of damage information selection and damage feature fusion, realizing a multi-level damage information extraction process from coarse to fine. The results show that the accuracy of damage severity assessment by the damage information extraction method with ladder feature mining is improved by more than 5% compared to other methods. The effectiveness and accuracy of the method in assessing the damage severity of multi-layer complex structures are demonstrated, providing a new perspective and solution for damage monitoring of multi-layer complex structures.

Lamb Wave-Based Damage Localization and Quantification in Composites Using Probabilistic Imaging Algorithm and Statistical Method.

Quantitatively and accurately monitoring the damage to composites is critical for estimating the remaining life of structures and determining whether maintenance is essential. This paper proposed an active sensing method for damage localization and quantification in composite plates. The probabilistic imaging algorithm and the statistical method were introduced to reduce the impact of composite anisotropy on the accuracy of damage detection. The matching pursuit decomposition (MPD) algorithm was utilized to extract the precise TOF for damage detection. The damage localization was realized by comprehensively evaluating the damage probability evaluation results of all sensing paths in the monitoring area. Meanwhile, the scattering source was recognized on the elliptical trajectory obtained through the TOF of each sensing path to estimate the damage size. Damage size was characterized by the Gaussian kernel probability density distribution of scattering sources. The algorithm was validated by through-thickness hole damages of various locations and sizes in composite plates. The experimental results demonstrated that the localization and quantification absolute error are within 11 mm and 2.2 mm, respectively, with a sensor spacing of 100 mm. The algorithm proposed in this paper can accurately locate and quantify damage in composite plate-like structures.

A New In Situ Coaxial Capacitive Sensor Network for Debris Monitoring of Lubricating Oil.

Wear debris monitoring of lubricant oil is an important method to determine the health and failure mode of key components such as bearings and gears in rotatory machines. The permittivity of lubricant oil can be changed when the wear debris enters the oil. Capacitive sensing methods showed potential in monitoring debris in lubricant due to the simple structure and good response. In order to improve the detection sensitivity and reliability, this study proposes a new coaxial capacitive sensor network featured with parallel curved electrodes and non-parallel plane electrodes. As a kind of through-flow sensor, the proposed capacitive sensor network can be in situ integrated into the oil pipeline. The theoretical models of sensing mechanisms were established to figure out the relationship between the two types of capacitive sensors in the sensor network. The intensity distributions of the electric field in the coaxial capacitive sensor network are simulated to verify the theoretical analysis, and the effects of different debris sizes and debris numbers on the capacitance values were also simulated. Finally, the theoretical model and simulation results were experimentally validated to verify the feasibility of the proposed sensor network.

Qualitative Classification of Lubricating Oil Wear Particle Morphology Based on Coaxial Capacitive Sensing Network and SVM.

In addition to lubricating and cooling, aero-engine lubricating oil is also a transport medium for wear particles generated by mechanical wear. Online identification of the number and shape of wear particles is an important means to directly determine the wear state of rotating parts, but most of the existing research focuses on the identification and counting of wear particles. In this paper, a qualitative classification method of wear particle morphology based on support vector machine is proposed by using the wear particle capacitance signal obtained by the coaxial capacitive sensing network. Firstly, the coaxial capacitive sensing network simulation model is used to obtain the capacitance signals of different shapes of wear particles entering the detection space of different electrode plates. In addition, a variety of intelligent optimization algorithms are used to optimize the relevant parameters of the support vector machine (SVM) model in order to improve the classification accuracy. By using the processed data and optimized parameters, a SVM-based qualitative classification model for wear particles is established. Finally, the validity of the classification model is verified by real wear particles of different sizes. The simulation and experimental results show that the qualitative classification of different wear particle morphologies can be achieved by using the coaxial capacitive sensing network signal and the SVM model.

Embedded FBG Sensor Based Impact Identification of CFRP Using Ensemble Learning.

Impact brings great threat to the composite structures that are extensively used in an aircraft. Therefore, it is necessary to develop an accurate and reliable impact monitoring method. In this paper, fiber Bragg grating (FBG) sensors are embedded in unidirectional carbon fiber reinforced plastics (CFRPs) during the manufacturing process to monitor the strain that is related to the elastic modulus and the state of resin. After that, an advanced impact identification model is proposed. Support vector regression (SVR) and a back propagation (BP) neural network are combined appropriately in this stacking-based ensemble learning model. Then, the model is trained and tested through hundreds of impacts, and the corresponding strain responses are recorded by the embedded FBG sensors. Finally, the performances of different models are compared, and the influence of the time of arrival (ToA) on the neural network is also explored. The results show that compared with a single neural network, ensemble learning has a better capability in impact identification.

Real-Time Life-Cycle Monitoring of Composite Structures Using Piezoelectric-Fiber Hybrid Sensor Network.

In this paper, an in situ piezoelectric-fiber hybrid sensor network was developed to monitor the life-cycle of carbon fiber-reinforced plastics (CFRPs), from the manufacturing phase to the life in service. The piezoelectric lead-zirconate titanate (PZT) sensors were inserted inside the composite structures during the manufacturing process to monitor important curing parameters, including the storage modulus of resin and the progress of the reaction (POR). The strain that is related to the storage modulus and the state of resin was measured by embedded fiber Bragg grating (FBG) sensors, and the gelation moment identified by the FBG sensors was very close to those determined by dynamic mechanical analysis (DMA) and POR. After curing, experiments were conducted on the fabricated CFRP specimen to investigate the damage identification capability of the embedded piezoelectric sensor network. Furthermore, a modified probability diagnostic imaging (PDI) algorithm with a dynamically adaptive shape factor and fusion frequency was proposed to indicate the damage location in the tested sample and to greatly improve the position precision. The experimental results demonstrated that the average relative distance error (RDE) of the modified PDI method was 68.48% and 46.97% lower than those of the conventional PDI method and the PDI method, respectively, with an averaged shape factor and fusion frequency, indicating the effectiveness and applicability of the proposed damage imaging method. It is obvious that the whole life-cycle of CFRPs can be effectively monitored by the piezoelectric-fiber hybrid sensor network.

The Design and Verification of an Active SAMSR Ultrasonic Guided Wave Monitoring System with Ultra-Low Crosstalk.

Due to long propagation distance and high sensitivity to a variety of damages, ultrasonic guided wave technologies have been widely applied in the damage detection or health monitoring of pipe networks and large plate-like structures. However, there are two important problems to be solved when applying this technology; namely, the large scanning time required for monitoring large-scaled structures and the serious crosstalk between the actuation and receiving signals, especially when monitoring hot-spot regions. Therefore, this study mainly designed key parts, such as the matrix switcher and attenuation circuit. The single-actuation and multiple-simultaneous-reception (SAMSR) mechanism based on an analog switching matrix and a low noise charge amplifier circuit was designed and integrated with the SPI control bus to shorten the scanning time. Moreover, a two-stage attenuation circuit with an interlocking isolation structure is presented to effectively isolate the receiving signals from the actuation signals to obtain ultra-low crosstalk even under a high voltage actuation source. In this study, the designed matrix switcher and other components were integrated into the developed ultrasonic guided wave monitoring system. Several experiments were conducted on a stiffened composite structure to illustrate the effectivity of the developed SAMSR ultrasonic guided wave system by comparing the signals collected with those from a commercial ultrasonic guided wave system.

An Eddy Current-Based Structural Health Monitoring Technique for Tracking Bolt Cracking.

Bolted joints are the primary structures for the load transfer of large-scale structures. It is vital to monitor the process of bolt cracking for enduring structural safety. In this paper, a structural health monitoring technique based on the embedding eddy current sensing film has been proposed to quantify the crack parameters of bolt cracking. Two configurations of the sensing film containing one-dimensional circumferential coil array and two-dimensional coil array are designed and verified to have the ability to identify three crack parameters: the crack angle, the crack depth, and the crack location in the axial direction of the bolt. The finite element method has been employed not only to verify the capacity of the sensing film, but also to investigate the interaction between the crack and the eddy current/magnetic field. It has been demonstrated that as the crack propagates, the variations of the induced voltage of the sensing coils are influenced by both eddy current effect and magnetic flux leakage, which play different roles in the different periods of the crack propagation. Experiments have been performed to verify the effectiveness and feasibility of the sensing film to quantify three crack parameters in the process of the bolt cracking.

Active Monitoring of Fatigue Crack in the Weld Zone of Bogie Frames Using Ultrasonic Guided Waves.

The bogie frame is an important structure of railway vehicles, transmitting the traction, braking force, lateral force, and vertical force during the traction operation. With the development of high speeds and heavy loads, the appearance of fatigue cracks in the bogie frames is increasing, which reduces the driving life of railway vehicles and even causes serious traffic accidents. Real-time monitoring on the integrity of the bogie is an inevitable requirement for ensuring the safe operation of railway vehicles. In this paper, ultrasonic guided wave-based active structural health monitoring (SHM) was developed to identify the fatigue crack of the bogie frame. Experiments were conducted on a welded T-shape specimen with a thickness of 12 mm. A total of 10 piezoelectric lead zirconate titanate (PZT) disks were mounted around the weld zone of the specimen, five of which were used as actuators, and the other five were used as sensors. Five-peak modulation narrow-band sine waves were input into the actuators to excite the specimen. From the sensor signals, the advanced damage index (DI) was calculated to identify the propagation of the crack. The experimental results demonstrate that crack damage as small as 2 mm in the weld zone of the bogie frame can be successfully detected. Some practical issues for implementing the SHM in real applications, such as crack quantification and environmental compensation, were also discussed.

Identification and Compensation Technique of Non-Uniform Temperature Field for Lamb Wave-and Multiple Sensors-Based Damage Detection.

Lamb wave-based damage detection for large-scale composites is one of the most prosperous structural health monitoring technologies for aircraft structures. However, the temperature has a significant effect on the amplitude and phase of the Lamb wave signal so that temperature compensation is always the focus problem. Especially, it is difficult to identify the damage in the aircraft structures when the temperature is not uniform. In this paper, a compensation method for Lamb wave-based damage detection within a non-uniform temperature field is proposed. Hilbert transform and Levenberg-Marquardt optimization algorithm are developed to extract the amplitude and phase variation caused by the change of temperature, which is used to establish a data-driven model for reconstructing the reference signal at a certain temperature. In the temperature compensation process, the current Lamb wave signal of each exciting-sensing path under the estimated structural condition is substituted into the data-driven model to identify an interpolated initial temperature field, which is further processed by an outlier removing algorithm to eliminate the effect of damage and get the actual non-uniform temperature field. Temperature compensation can be achieved by reconstructing the reference signals within the identified non-uniform temperature field, which are used to compare with the current acquired signals for damage imaging. Both simulation and experiment were conducted to verify the feasibility and effectiveness of the proposed non-uniform temperature field identification and compensation technique for Lamb wave-based structural health monitoring.

Piezoelectric Transducer-Based Structural Health Monitoring for Aircraft Applications.

Structural health monitoring (SHM) is being widely evaluated by the aerospace industry as a method to improve the safety and reliability of aircraft structures and also reduce operational cost. Built-in sensor networks on an aircraft structure can provide crucial information regarding the condition, damage state and/or service environment of the structure. Among the various types of transducers used for SHM, piezoelectric materials are widely used because they can be employed as either actuators or sensors due to their piezoelectric effect and vice versa. This paper provides a brief overview of piezoelectric transducer-based SHM system technology developed for aircraft applications in the past two decades. The requirements for practical implementation and use of structural health monitoring systems in aircraft application are then introduced. State-of-the-art techniques for solving some practical issues, such as sensor network integration, scalability to large structures, reliability and effect of environmental conditions, robust damage detection and quantification are discussed. Development trend of SHM technology is also discussed.

A Flexible Capacitive Pressure Sensor Based on Ionic Liquid.

A flexible microfluidic super-capacitive pressure sensor is developed to measure the surface pressure of a complex structure. The innovative sensor contains a filter paper filled with ionic liquid, and coated with two indium tin oxide polyethylene terephthalate (ITO-PET) films on the top and bottom, respectively. When external pressure is applied on the top ITO-PET film of the sensor mounted on the surface of an aircraft, the capacitance between the two ITO-PET films will change because of the deformation of the top ITO-PET film. The external pressure will be determined based on the change of the capacitance. Compared to the traditional pressure sensor, the developed sensor provides a high sensitivity of up to 178.5 nF/KPa and rapid dynamic responses for pressure measurement. Meanwhile, experiments are also conducted to study the influence of the thickness of the sensing film, sensing area, temperature, and humidity.

High Strain Survivability of Piezoceramics by Optimal Bonding Adhesive Design.

As one of the most common transducers used in structural health monitoring (SHM), piezoceramic sensors can play an important role in both damage detection and impact monitoring. However, the low tensile strain survivability of piezoceramics resulting from the material nature significantly limits their application on SHM in the aerospace industry. This paper proposes a novel approach to greatly improve the strain survivability of piezoceramics by optimal design of the adhesive used to bond them to the host structure. Theoretical model for determining the strain transfer coefficient through bonded adhesive from the host structure to piezoceramic is first established. Finite element analysis is then utilized to study the parameters of adhesive, including thickness and shear modulus. Experiments are finally conducted to validate the proposed method, and results show the piezoceramic sensors still work well when they are bonded on the host structures with tensile strain up to 4000 µÎµ by using the optimal adhesive.

Characteristics Study of In-Situ Capacitive Sensor for Monitoring Lubrication Oil Debris.

As an essential part of engine health monitoring (EHM), online lubrication oil debris monitoring has recently received great attention for the assessment of rotating and reciprocating parts in aero-engines, due to its high integration, low cost and safe characteristics. However, it is be a challenge to find a suitable sensor operating in such a complex environment. We present an unconventional novel approach, in which a cylinder capacitive sensor is designed and integrated with the pipeline of an engine lubrication system, so that the capacitive sensor can effectively detect changes in the lubrication oil condition. In this paper, an attempt to illustrate the performance characteristics of the developed cylinder capacitive sensor is made, through an experiment system that simulates a real scenario of a lubrication oil system. The main aim of the research was to qualitatively describe the relationship between the sensor parameter and the lubrication oil debris. In addition, the effect of the temperature and flow rate of the lubrication oil on capacitance change was performed by several experiments and we figured out a compensation method. The experimental results demonstrated that the cylinder capacitive sensor can potentially be used for lubrication oil debris monitoring of the health condition of an aero-engine.

Impact monitoring of large size complex metal structures based on sparse sensor array and transfer learning.

During aircraft operations, the impact events experienced by the aircraft may cause damage to the structure, thus posing a safety hazard. Therefore, an accurate determination of where the impact occurred and the time history of the impact force can provide an important basis for assessing the condition of the aircraft. However, modern aircraft structures are often large and complex, and relying on dense arrays of sensors for monitoring adds additional weight to the aircraft and reduces the economics of aircraft operation. This paper proposes a region-to-point monitoring strategy. First, a Convolutional Neural Network (CNN) model with region localization capability is trained using the sparse sensor array acquisition data. Then, the weighted center algorithm is used to determine the specific location where the impact occurs, in which the added fuzzy genetic algorithm can provide the ability to adjust weights to improve localization accuracy adaptively. As for the impact force prediction, this paper adopts a model based on a Convolutional Neural Network-Gated Recurrent Unit combined with a Squeeze-Excitation attention mechanism (CNN-GRU-SE), which is capable of predicting the impact force occurring in the flat plate and reinforced structure region of the aircraft under different energy conditions. Finally, the impact of incorporating a transfer learning approach on model performance and training cost is investigated for fuselage regions with different structures.

Distributed Estimation With Cross-Verification Under False Data-Injection Attacks.

Under false data-injection (FDI) attacks, the data of some agents are tampered with by the FDI attackers, which causes that the distributed algorithm cannot estimate the ideal unknown parameter. Due to the concealment of the malicious data tampered with by the FDI attacks, many detection algorithms against FDI attacks often have poor detection results or low detection efficiencies. To solve these problems, a conveniently distributed diffusion least-mean-square (DLMS) algorithm with cross-verification (CV) is proposed against FDI attacks. The proposed DLMS with CV (DLMS-CV) algorithm is comprised of two subsystems: one subsystem provides a detection test of agents based on the CV mechanism, while the other provides a secure distribution estimation. In the CV mechanism, a smoothness strategy is introduced, which can improve the detection performance. The convergence performance of the proposed algorithm is analyzed, and then the design method of the adaptive threshold is also formulated. In particular, the probabilities of missing alarm and false alarm are examined, and they decay exponentially to zero under sufficiently small step size. Finally, simulation experiments are provided to illustrate the effectiveness and simplicity of the proposed DLMS-CV algorithm in comparison to other algorithms against FDI attacks.

Corrosion quantification of plate-type structures using Lamb wavefield and monogenic signal processing.

Corrosion is one of the most common damage types which seriously affects structural safety. In this paper, a Lamb wavefield-based monogenic signal processing algorithm is proposed to quantify the corrosion parameters, including location, area, shape and depth, in plate-type structures. The monogenic signal processing based on Riesz transform will cause a serious problem, that is, phase wrapping. To solve this problem, a robust fast phase unwrapping algorithm is developed. Then, the phase spatial distribution of the extracted Lamb wavefield can be extracted, which can be used to calculate the wavenumber distribution. The wavenumber distribution is related to the structure thickness or corrosion depth, which can be further used for corrosion imaging. Simulated Lamb wavefield signals calculated by finite element simulation are employed to evaluate the parameters of circular corrosion and complex umbrella corrosion. The results show that the proposed algorithm has a great advantage in corrosion identification accuracy and calculation time compared with the existing algorithms. A completely non-contact laser ultrasonic system is established for acquiring Lamb wavefield containing square corrosion, and it is proved that the proposed algorithm is able to quantify the corrosion location, area, shape and depth with good accuracy in the experiment.

A transformer model with enhanced feature learning and its application in rotating machinery diagnosis.

Deep learning has become the prevailing trend of intelligent fault diagnosis for rotating machines. Compared to early-stage methods, deep learning methods use automatic feature extraction instead of manual feature design. However, conventional intelligent diagnosis models are trapped by a dilemma that simple models are unable to tackle difficult cases, while complicated models are likely to over-parameterize. In this paper, a transformer-based model, Periodic Representations for Transformers (PRT) is proposed. PRT uses a dense-overlapping split strategy to enhance the feature learning inside sequence patches. Combined with the inherent capability of capturing long range dependencies of transformer, and the further information extraction of class-attention, PRT has excellent feature extraction abilities and could capture characteristic features directly from raw vibration signals. Moreover, PRT adopts a two-stage positional encoding method to encode position information both among and inside patches, which could adapt to different input lengths. A novel inference method to use larger inference sample sizes is further proposed to improve the performance of PRT. The effectiveness of PRT is verified on two datasets, where it achieves comparable and even better accuracies than the benchmark and state-of-the-art methods. PRT has the least FLOPs among the best performing models and could be further improved by the inference strategy, reaching an accuracy near 100%.

A New Laser Ultrasonic Inspection Method for the Detection of Multiple Delamination Defects.

Delamination is one of the most common types of defects for carbon fiber reinforced plastic (CFRP) composites. The application of laser techniques to detect delamination faces difficulties with ultrasonic wave excitation because of its low thermal conductivity. Much of the research that can be found in the literature has only focused on the detection of a single delamination. In this study, aluminum foil was pasted onto the surface of the composite so that it was vulnerable to ablation and could acquire a usable signal. Using a fully noncontact system with laser excitation at a fixed point and a scanning laser sensor, the effects of different aluminum foil sizes and shapes on the wavefield were studied for the composites; we decided to use a rectangle with 3 mm length and 5 mm width for laser excitation experiments. Wavefield characteristics of the composite plates were analyzed with single- and multi-layered Teflon inserts. Taking the time window for standard ultrasonic testing as a reference, the algorithms for localized wave energy with appropriate time windows are presented for the detection of single and multiple defects. The appropriate time window is meaningful for identifying each delamination defect. The algorithm performs well in delamination detection of the composites with one or multiple Teflon inserts.

Multi-frequency localized wave energy for delamination identification using laser ultrasonic guided wave.

Delamination is the most common and dangerous failure mode for multilayered structures. Delamination defects of different shapes and sizes have different sensitivity to guided wave of different frequencies and modes. So that it is necessary to study the application of multi-frequency methods for achieving detection. In this study, the algorithm of multi-frequency localized wave energy is present using laser ultrasonic guided waves for delamination identification. Localized wave energy is acoustic energy in space under specific wavenumber. New wavenumber components occur in damaged composite plates and its localized wave energy can be used for delamination identification. The localized wave energy is not only related to mode conversion caused by the decrease of structural thickness above the delamination, but also the scattering waves in delamination region. The scattering waves make acoustic energy redistributed and it is enhanced at specific spatial position. The discovery has been verified in simulation and experiment. Multi-frequency experimental results show lower noises and more discernible profile of delamination region in two specimens, including medial and non-medial delamination. In the case of medial delamination, the actual dispersion curve is closer to the dispersion curve of upper laminate at high frequency; in the case of non-medial delamination, the actual dispersion curve is similar to the ideal situation ignoring the effect of epoxy resin. Based on the actual dispersion curves, two critical parameters of proper frequencies and filter threshold are selected for delamination identification using laser ultrasonic guided wave.