Damage Position Research Articles

Detection of Closing Cracks in Beams Based on Responses Induced by Harmonic Excitation.

The non-linear contact model was chosen to simulate a closed crack in a cantilever beam. This study examines the shape and characteristics of the phase diagram of a cantilever beam with closed cracks. It investigates how various crack properties influence the geometry of the phase diagram and proposes a method for identifying cracks based on their features. The area of each closed curve in the phase diagram was determined using the pixel method. Based on the results, the contact model proved effective in simulating closed cracks and was sensitive to nonlinear closing cracks. The vibration responses of beams with different damage severities and positions exhibited distinct geometric features. The crack parameter was identified by locating the intersection of contour lines on the maps. According to numerical calculations, the phase diagrams for super-harmonic resonance seem to be more susceptible to changes in closed cracks with varied damage locations and severities. The wavelet transform was also employed to identify closed cracks using RMS signals, and the results were compared with those obtained from the phase diagram.

Two-Dimensional Damage Localization Using a Piezoelectric Smart Aggregate Approach-Implementation on Arbitrary Shaped Concrete Plates.

This paper presents the application of a hybrid approach for damage localization in concrete plates of arbitrary geometric shapes and a constant thickness. The hybrid algorithm utilizes fast discrete wavelet transformation, energy approach and time of flight criteria for the purpose of the localization of single- and multi-damage problems inside or on the periphery of concrete plates. A brief theoretical background of the hybrid method as well as numerical procedures for modeling the piezoelectric smart aggregate and ultrasonic wave propagation are presented. Experimental and numerical verification of the damage localization were performed on square samples/models with one or two damages and with 16 positions of piezoelectric smart actuator/sensor aggregates. After the verification of the hybrid method, a numerical simulation was performed on models with one or two damages for plates of arbitrary geometric shapes. Based on the obtained results, it was concluded that the proposed method can be applied to damage localization in concrete plates of arbitrary geometric shapes. The presented method and numerical procedure can be further used in research through varying the geometry, number and position of damages as well as the number and position of piezoelectric smart aggregates.

Time-varying damage detection in beam structures using variational mode decomposition and continuous wavelet transform

Civil engineering structures in operation are likely to suffer damage. During the service life, structural damage evolves gradually from minor to severe. However, most current studies focus on damage localization and quantification of damage severity, and hence little attention has been paid to the detection of time-varying damage. This paper aims to propose a new approach for tracking the damage evolution of beam structures. In this approach, the wavelet threshold method is used at first to denoise the response signal. After that, the variational mode decomposition (VMD) is introduced to decompose the denoised response signal into several mono-components adaptively. A proposed index of wavelet total energy change (WTEC) is then applied to the new decomposed signals to localize damages of beam structures. On a basis of this, a time-varying damage index called wavelet energy change ratio (WECR) is established via the continuous wavelet transform and time window techniques and then applied to the response at the damaged position for tracking damage evolution. The proposed method is verified via a numerical example of a simply supported beam (its length and area of cross section are 5 m and 0.04 m2, respectively) with a sudden and a linear stiffness reduction under 1940 El Centro ground acceleration record. In addition, an experiment on a 10-meters-long steel bridge with abrupt stiffness reduction under a moving load is investigated to demonstrate the effectiveness and accuracy of the proposed time-varying damage detection method. The results show that the index of WTEC is able to localize damages of beam structures effectively whatever they are a single damage position or multiple damage positions. Moreover, the damage evolution can be tracked by the proposed index of WECR accurately even though random noises and end effects pose a great impact on the time-varying damage detection results.

Design of submarine pipeline inspection system based on ultrasound

A design of a non-destructive testing system for underwater pipelines based on ultrasound has been proposed, consisting of a three-axis control system and an ultrasonic thickness measurement system. This can achieve full coverage testing and real-time data transmission of pipeline wall thickness through integrated composite rotating mechanisms, travel driving mechanisms, pipeline surface preprocessing mechanisms, data acquisition systems, etc., with high automation and the ability to achieve pipeline based testing. Among them, the pipeline surface pretreatment mechanism includes a polishing mechanism and a circulating immersion system, which are used to improve the coupling effect between the pipeline wall and the ultrasonic probe. This non-destructive testing system can achieve high detection efficiency and testing accuracy, effectively solving the current problems of undersea pipeline leakage and inaccurate positioning of corrosion damage. At present, the problem of global and efficient scanning still needs to be solved, as otherwise it will be difficult to further improve work efficiency. Highlights A design of a non-destructive testing system for underwater pipelines based on ultrasound has been proposed, consisting of a three-axis control system and an ultrasonic thickness measurement system The system can realize scanning detection with high efficiency and automation The system is equipped with an automatic coupling mechanism to ensure the stability and reliability of the work

Experimental Study on Bridge Structural Damage Identification Based on Quasi-Static Displacement Effects and Wavelet Packet Decomposition

Based on the displacement-induced linearity error curve and the theory of wavelet packet analysis, a bridge structural damage identification method is proposed, integrating two damage indicators: Quasi-Static Displacement-Induced Linearity Error Curve (QSDIL) and Relative Energy Rate of Wavelet Packet Energy Spectrum (RES). This method first constructs the QSDIL damage indicator based on a quasi-static displacement-induced linearity error, which is used for the preliminary localization of the bridge structural damage. Subsequently, relying on the principles of wavelet packet analysis, the method constructs the RES damage indicator for accurate positioning of the damage location in the bridge structure. The proposed method is experimentally validated, and the impact of factors such as single-point damage, multi-point damage, signal noise, lane position, and vehicle weight on the experimental results is investigated. The results indicate that the proposed method exhibits excellent identification performance for the location of structural damage in both single-point and multi-point damage scenarios, with good agreement between experimental and theoretical values. As the signal-to-noise ratio decreases, the accuracy and precision of the RES curve in locating the bridge structural damage position exhibit a nonlinear decreasing trend, with relatively small identification errors observed at noise levels of 90 dB to 100 dB. Different lane positions have a minimal impact on the damage identification effectiveness. With increasing vehicle weight, both QSDIL and RES curves show an increasing trend in peak values, facilitating the localization of bridge structural damage positions.

Analysis of Failure Forms of Bevel Gear, Shaft, and Bearing Structure System Considering Multi-Mode Damage Accumulation

Abstract During the working process, the structural system, including bevel gear, shaft, and bearing, located in the front journal of the high pressure compressor of the aeroengine rotor is affected by various loads such as assembly load, centrifugal load, temperature load, and meshing excitation, and it leads to different modes of interface damage, for example, wear, slip, and fatigue. With the increase number of working cycles, different interface damage modes affect each other and damage gradually accumulates, which leads to the degradation of the mechanical properties of each component. Finally, the function of the weakest component is lost, resulting in the failure of the structural system. Consequently, the comprehensive influence of multiple failure forms caused by interface damage at different positions results in the failure of the structural system, where the damage position and the failure position are different, and the final failure form is uncertain. In this paper, a damage accumulation mechanical model considering multimode damage interaction is established for bevel gear, shaft and bearing structure system. And the damage-failure mechanical process of the structural system and its key influencing factors under complex environment loads are studied. The results showed that the failure forms of the structural system were closely related to the assembly and load characteristic parameters of the components. Therefore, considering the randomness of the initial assembly state and environment loads, the damage accumulation mechanical process and the final failure forms of the structural system have significant uncertainty.

A damage-informed neural network framework for structural damage identification

In this work, an effective Damage-Informed Neural Network (DINN) is first developed to pinpoint the position and extent of structural damage. Instead of resolving the damage identification problem by conventional numerical methods, a Deep Neural Network (DNN) is employed to minimize the loss function which is designed by combining multiple damage location assurance criterion and flexibility matrices to guide the training process. In our computational framework, the parameters of the network, which include both weights and biases, are treated as new design variables instead of damage ratios. Therein, the training data consists only of a set of spatial coordinates of elements, whilst corresponding the damaged ratios of elements unknown to the network are factored into the output. To achieve the goal, the loss value is calculated relying on the predicted damage ratios with supporting Finite Element Analysis (FEA). Additionally, Bayesian Optimization (BO) algorithm is used to automatically tune hyperparameters of the network for enhancing reliability in damage identification. Several numerical examples for damage localization of truss and frame structures are investigated to evaluate the effectiveness and reliability of the suggested methodology. The obtained results point out that our model not only correctly locates the actual damage sites but also requires the least number of structural analyses and faster convergence rate compared with other algorithms.

A machine-learning approach to setting optimal thresholds and its application in rolling bearing fault diagnosis

Bearings are one of the critical components of any mechanical equipment. They induce most equipment faults, and their health status directly impacts the overall performance of equipment. Therefore, effective bearing fault diagnosis is essential, as it helps maintain the equipment stability, increasing economic benefits through timely maintenance. Currently, most studies focus on extracting fault features, with limited attention to establishing fault thresholds. As a result, these thresholds are challenging to utilize in the automatic monitoring diagnosis of intelligent devices. This study employed the generalized fractal dimensions to effectively extract the feature of time-domain vibration signals of bearings. The optimal fault threshold model was developed using the receiver operating characteristic curve, which served as the baseline of exception judgment. The extracted fault threshold model was verified using two bearing operation experiments. The experimental results revealed different damaged positions and components observed in the two experiments. The same fault threshold model was obtained using the method proposed in this study, and it effectively diagnosed the abnormal states within the signals. This finding confirms the effectiveness of the diagnostic method proposed in this study.

Combining circular laser sensing array with MUSIC algorithm for fast damage localization

Large plate shell structures are key supporting components for aerospace and transportation equipment. Rapid localization of damages in large-scale thin-walled structures is an urgently needed technology for on-site detection and maintenance. This paper proposed a rapid damage localization method based on a laser ultrasonic circular arrays, combining the advantages of the circular arrays and the MUSIC algorithm. It does not need reference signals in advance, the direct waves and scattered waves can be quickly distinguished and extracted according to the time of arrival (TOA). Then, the two-dimensional multiple signal classification method(2D-MUSIC) under near-field conditions is used to quickly locate damage, which is a feature subspace algorithm in array signal processing technology. Compared with other commonly used imaging algorithms, this method has great advantages in detection efficiency and angular resolution. According to the detection method proposed in this paper, the position of single damage and multiple incoherent damages within 0.12 m2 can be quickly found within 1.3 s, and the angle error and distance error are not more than 5° and 5 cm. However, for the damage of coherent sources, there may be artifacts interfering with the imaging results.

Crack localisation in composite cantilever beams using natural frequency measurements

ABSTRACT Crack localisation is a fundamental step of damage identification. Most of the published research on crack localisation based on natural frequency consists in model updating which requires a great deal of calculation. This paper presents a simple method of crack localisation in composite cantilever beam-type structures. The proposed method consists of classifying the reduction of the first four measured natural frequencies and deducing the damage position from a table. For these purposes, a finite element model is first used to study the direct problem: the vibration behaviour of an intact beam and the changes caused in the damaged state. Then it is shown that the classification of the frequency reduction ratio is different for each damaged zone. Consequently, the beam is discretized into several areas according to the classifications of the first four frequency ratios. Therefore, this discretization is represented in a table to be used in damage localisation. The robustness of the suggested technique is first tested numerically by using the Abaqus/CAE finite element software environment. Then, the applicability of the localisation method is validated experimentally on a cracked glass fibre-reinforced epoxy beam. The results show that the proposed method can successfully localise damage in composite beams.

Model Updating Based on Bayesian Theory and Improved Objective Function

Model updating is the process of calibrating model parameters to improve the accuracy of numerical prediction. To improve the accuracy and efficiency of model updating, this paper proposes a model updating method based on Bayesian theory and improved objective function. A natural frequency damage index is proposed based on the Bayesian theory, which is calculated according to the established damage position function and the measured frequency data. The distribution of the index can determine the damage location and the number of updated parameters for model updating. An objective function with weight terms is proposed based on strain assurance criterion to describes the difference between the finite element model and the actual structure, and the weight term of the objective function is determined by the sensitivity coefficient. Examples show that the improved model updating method is more accurate and efficient.

Structural Damage Detection Technique of Secondary Building Components Using Piezoelectric Sensors

With demand for the long-term continued use of existing building facilities, structural health monitoring and damage detection are attracting interest from society. Sensors of various types have been practically applied in the industry to satisfy this need. Among the sensors, piezoelectric sensors are an extremely promising technology by virtue of their cost advantages and durability. Although they have been used in aerospace and civil engineering, their application for building engineering remains limited. Remarkably, recent catastrophic seismic events have further reinforced the necessity of rapid damage detection and quick judgment about the safe use of facilities. Faced with these circumstances, this study was conducted to assess the applicability of piezoelectric sensors to detect damage to building components stemming from concrete cracks and local buckling. Specifically, this study emphasizes structural damage caused by earthquakes. After first applying them to cyclic loading tests to composite beam component specimens and steel frame subassemblies with a folded roof plate, the prospective damage positions were also found using finite element analysis. Crack propagation and buckling locations were predicted adequately. The piezoelectric sensors provided output when the concrete slab showed tensile cracks or when the folded roof plate experienced local buckling. Furthermore, damage expansion and progression were detected multiple times during loading tests. Results showed that the piezoelectric sensors can detect the structural damage of building components, demonstrating their potential for use in inexpensive and stable monitoring systems.

Damage Identification of Rotating Blades Based on Fuzzy C-Means and Frequency Response Function Curvature Methods

Rotor blades are one of the key components of helicopter. If the blades are damaged, the safety and reliability of the helicopter will be seriously affected. Therefore, it is necessary to investigate the damage identification of the rotating blades. In this paper, a rotating cantilever beam is used to model the rotor blade. Based on the assumed mode method and Hamilton’s principle, the equation of motion is formulated, and the correctness of the model is verified by numerical and experimental studies. Altogether, two methods are used to identify the damages on the blade. The first one is the cluster analysis method based on the fuzzy C-mean theory. In order to reduce the dimension of the signal features, the singular value decomposition is introduced. The second method is the curvature of frequency response function method that can be used to determine the exact position of damages. Simulation results show that one can use fuzzy C-mean method to determine whether there is damage on the blade firstly and then determine the exact position of the damage through the curvature of frequency response function method.

Experimental study on buckling behavior of composite cylindrical shells with and without circular holes under hydrostatic pressure

Composite materials have been widely used in the field of pressure shells for underwater vehicles due to their great potential. This paper presents an experimental study of composite cylindrical shells with and without the circular hole subjected to external hydrostatic pressure. The mechanical response of the composite cylindrical shells under external hydrostatic pressure is recorded by the strain acquisition system, and the buckling deformation process of the shells is also captured by the high-speed camera system. According to the experimental results, the entire compression process of the shell can be divided into three stages based on variations in strain. Based on the findings that buckling starts very early and accounts for about 35 % of the entire process, the concept of initial buckling pressure is proposed to distinguish the critical point of the shell transition from a stable state to an unstable state. It is found that the damage positions of the three shells all correspond to the troughs in the buckling waveform, and the final fiber breakage of the perforated shells with the reinforcements both occur near the reinforcement component. Finally, a comparative analysis of the strain response of the shell with the hole and the complete shell is carried out to explore their similarities and differences. The experimental results in this paper provide valuable information for the design of perforated composite pressure shells for underwater vehicles.

Simulation Study on Axial Location Identification of Damage in Layered Pipeline Structures Based on Damage Index

This study investigates the feasibility of identifying the axial position of circumferential defects in laminated pipeline structures based on damage indices. Wavelet packet decomposition is combined with damage indices, and the effects of dual defects with the same circumferential position but different axial positions, as well as dual defects with different circumferential and axial positions, on damage indices are separately studied. Our aim was to determine the potential to use damage indices to identify the axial position of circumferential defects in laminated pipeline structures. ABAQUS finite element analysis software was used to establish models of laminated pipeline structures with single defects and dual defects (with the same circumferential position but different axial positions, and with different circumferential and axial positions). The laminated pipeline structure was composed of a steel pipe (structural layer), a rigid polyurethane foam (insulation layer), and a high-density polyethylene (anticorrosion layer). The received sensing signals were averaged, and subjected to 5-level wavelet packet decomposition, to calculate the damage index values, which were then organized into a damage index matrix. Based on the trend of changes in the damage index matrix, the effects of variations in the number and circumferential position of the defects on the identification of the axial position of the damage were analyzed. The results indicate that the trend in damage index changes is influenced by the number of defects, and the increase in the circumferential distance between the second and the piezoelectric element sensor. This study found that when 1.7λ≤PD≤3.4λ, Idouble defect 90°<Isingle defect<Idouble defect 0°; when 3.7λ≤PD≤4λ, Idouble defect 90°<0.3<Idouble defect 0°<Isingle defect. This article demonstrates that the identification of the axial position of damage in laminated pipeline structures can be achieved using the damage index values in the damage index matrix. Additionally, this damage identification method overcomes the limitation of the wavelet packet’s inability to identify dual defects with relatively small relative axial distances. This provides new ideas and methods for finite element analysis in identifying the axial position of damage in laminated pipeline structures.

Exploration of Damage Identification Method for a Large-Span Timber Lattice Shell Structure in Taiyuan Botanical Garden based on Structural Health Monitoring.

Large-span spatial lattice structures generally have characteristics such as incomplete modal information, high modal density, and high degrees of freedom. To address the problem of misjudgment in the damage detection of large-span spatial structures caused by these characteristics, this paper proposed a damage identification method based on time series models. Firstly, the order of the autoregressive moving average (ARMA) model was selected based on the Akaike information criterion (AIC). Then, the long autoregressive method was used to estimate the parameters of the ARMA model and extract the residual sequence of the autocorrelation part of the model. Furthermore, principal component analysis (PCA) was introduced to reduce the dimensionality of the model while retaining the characteristic values. Finally, the Mahalanobis distance (MD) was used to construct the damage sensitive feature (DSF). The dome of Taiyuan Botanical Garden in China is one of the largest non-triangular timber lattice shells worldwide. Relying on the structural health monitoring (SHM) project of this structure, this paper verified the effectiveness of the damage identification model through numerical simulation and determined the damage degree of the dome structure through SHM measurement data. The results demonstrated that the proposed damage identification method can effectively identify the damage of large-span timber lattice structures, locate the damage position, and estimate the degree of damage. The constructed DSF had relatively strong robustness to small damage and environmental noise and has practical application value for SHM in engineering.

Novel Approach-Based Sparsity for Damage Localization in Functionally Graded Material

Model-based approaches have been widely employed in damage detection and localization studies. However, alternative techniques, such as built-in online detection methods, hold promise for future advancements in structural health monitoring technologies. In this research paper, we present a dynamic algorithm specifically designed for accurate damage localization in functionally graded plates. The suggested method involves the creation of a grid matrix that captures the dynamic response of the structure over time. Subsequently, an optimization process is performed using a linear equation that incorporates the information contained within the grid, enabling the precise localization of damage. To address the inherent sparsity of the localization nature, we utilize the FISTA (fast iterative shrinkage-thresholding algorithm) as a problem solver. The effectiveness of our approach is evaluated through experimental tests on a functionally graded plate with clamped free boundary conditions. Multiple damage scenarios are investigated, including cases with damage signals on and off-the-grid. The results demonstrate that our proposed approach is capable of accurately predicting the position of damage, indicating its suitability for application in low-size data systems.

Accelerometer Mass Loading Study Based on a Damage Identification Method using Fundamental Laws in Closed Systems

Many civil engineering structures can be evaluated as closed systems in which additional mass spatial distribution can change within the system in terms of the fundamental continuity and energy equations. Considering the heavy hours of traffic, it can be assumed that the position of the vehicle mass on the bridge is variable, but the total mass remains constant. This study aims to show that the damage to the closed systems can be successfully estimated by using a damage indicator that is valid for systems with constant mass distribution. In the analytical study, a 100-element 200 degree of freedom fixed-free beam is investigated and the assessment of the damage position is verified numerically and the validity of the parameter is examined in an experimental study. Except for the piece at the free end of the beam; It has been determined that the damage indicator calculated on the damaged elements is 60 times larger than the damage indicator calculated at the undamaged elements. In the elements at the free end of the beam; it was observed that this ratio is between 6 and 40 depending on the mass of the accelerometer. Therefore, a criterion for accelerometer mass and damage indicator is proposed.

Development and validation of a probabilistic multistage algorithm for damage localization in piezo-monitored structures

All structures during operating life can be affected by faults induced by accidental events and operational conditions. Structural health monitoring systems can provide quasi-real-time diagnosis of the structure, thus enabling the condition-based maintenance approach. By means of piezoelectric transducers (PZTs) and ultrasonic guided waves (UGW), the structural integrity can be easily interrogated, even though laborious post-processing techniques are required to correctly interpret sensed data. This work aims to devise a new automatic diagnosis framework based on the propagation of UGW for thin-walled structures fault detection and localisation. Specifically, a fully automated damage identification algorithm was developed through a numerical dataset obtained by finite element simulations, and then validated experimentally. The case of study consisted of a square-shaped aluminium plate equipped with a five PZTs network. Five different damage positions and three different damage sizes were considered. The originality of the proposed algorithm lies in the data processing methodology as well as in its capability to detect damages located inside and outside the sensors network, even close to the panel edges. Algorithm provides, in less than 15 s, indications on the possible damage location and related probability position with a reduced dispersion with respect to other algorithms proposed in literature. A clear image is created displaying the damage position map. The visualisation of the damage position map on the surface of the monitored part allows successful damage imaging and would enable operators to address more efficiently the inspection procedures only in the highlighted areas, reducing maintenance and repair expenses.

1D CNN Based Detection and Localisation of Defective Droppers in Railway Catenary

Defective droppers pose a significant threat to the performance of the contact between the train pantograph and railway catenary. In this paper, the impact of damaged droppers on the performance of pantograph–catenary interaction behaviour is analysed, and the impact of varying degrees of damage to each dropper is labelled. To improve the classification accuracy when both the damage degree and position are considered, a model integrating multiple 1D CNNs is proposed. Approaches including randomly searching the optimal hyper-parameters and K-fold cross-validation are used to prevent overfitting and to ensure model performance regardless of the training data subset selected. Compared with a conventional 1D CNN, the classification performance of the integrated method is demonstrated using the metrics accuracy, F1-score, precision and recall. It is concluded that, through the use of the integrated 1D CNN, damaged droppers can be detected and localised based on the pantograph–catenary contact force. Hence, intelligent catenary inspection can be enhanced.