Damage Detection Methodology Research Articles

Multi-damage detection of composite blades via the curvature modal shape approach in combination with surface interpolation and extreme learning machine

In recent years, there has been a rise in renewable energy, which has led to an increase in equipment maintenance. The damage detection technique for wind turbine blades (WTBs) can reduce maintenance costs. The majority are not immediately applicable to structures that are in an operating condition. Motivated by the current constraints, a novel two-stage multi-damage quantitative detection approach in WTBs during operation has been proposed. The multiple damage locations are first identified by calculating the curvature modal shape (CMS) difference for the WTBs after the interpolation of the operating deflection shape (ODS) using surface interpolation (SI). Then the relationship between the natural frequencies and damage severities for the WTBs is determined utilizing the finite element method (FEM). The established correlation provides a natural frequency database that can be utilized for identifying the corresponding damage severities through the application of the Extreme Learning Machine (ELM) method, upon measuring the natural frequencies of the damaged WTB. The efficacy of the suggested damage detection methodology has been confirmed through simulations conducted on WTBs featuring two or three instances of damage. Moreover, the ODS along with natural frequencies of the WTBs exhibiting two or three damages are experimentally assessed using a Scanning Laser Doppler Vibrometer. The simulation and experiment verify that the presented method has satisfactory effectiveness in damage detection.

A Fault Diagnosis Approach Utilizing Artificial Intelligence for Maritime Power Systems within an Integrated Digital Twin Framework

This research focuses on enhancing the preventive maintenance strategies currently employed for induction motors within ship propulsion systems, advocating for a shift towards a predictive maintenance model. It introduces a real-time monitoring framework that continuously observes the induction motor, providing essential support to maintenance personnel. The motor operates under a range of environmental and operational conditions, including temperature fluctuations, rotational speeds, and mechanical loads. These variations can obscure the current time series data, potentially masking signs of actual damage and hindering effective damage detection. To tackle this issue, the proposed framework utilizes artificial intelligence (AI) technology, specifically the well-established autoencoder, in conjunction with the Mahalanobis statistical distance. This approach accounts for the diverse operating conditions during the training phase, allowing it to model complex, non-linear relationships and effectively differentiate between normal and anomalous states. The framework is integrated into a decision support platform designed for real-time operations in maritime settings, offering a sophisticated system architecture that aims to align advanced damage detection methodologies with the maritime industry’s need for real-time, user-friendly solutions.

Damage Detection and Localization Methodology Based on Strain Measurements and Finite Element Analysis: Structural Health Monitoring in the Context of Industry 4.0

This paper investigates the integration of Structural Health Monitoring (SHM) within the frame of Industry 4.0 (I4.0) technologies, highlighting the potential for intelligent infrastructure management through the utilization of big data analytics, machine learning (ML), and the Internet of Things (IoT). This study presents a success case focused on a novel SHM methodology for detecting and locating damages in metallic aircraft structures, employing dimensional reduction techniques such as Principal Component Analysis (PCA). By analyzing strain data collected from a network of sensors and comparing it to a baseline pristine condition, the methodology aims to identify subtle changes in local strain distribution indicative of damage. Through extensive Finite Element Analysis (FEA) simulations and a PCA contribution analysis, the research explores the influence of various factors on damage detection, including sensor placement, noise levels, and damage size and type. The findings demonstrate the effectiveness of the proposed methodology in detecting cracks and holes as small as 2 mm in length, showcasing the potential for early damage identification and targeted interventions in diverse sectors such as aerospace, civil engineering, and manufacturing. Ultimately, this paper underscores the synergistic relationship between SHM and I4.0, paving the way for a future of intelligent, resilient, and sustainable infrastructure.

Drive-by damage detection methodology for high-speed railway bridges using sparse autoencoders

Drive-by damage detection methodology for high-speed railway bridges using sparse autoencoders

Introduction and application of a drive-by damage detection methodology for bridges using variational mode decomposition

In this research, the variational mode decomposition (VMD) method is used for the drive-by health monitoring of bridges. Firstly, the problem of a half-trailer tractor moving over a bridge is formulated. Next, a Finite Element (FE) code is developed and verified against modal analysis results where complete agreement is found. The vehicle's output signals are decomposed through VMD and then analyzed to identify and precisely locate damage in the bridge structure. The range of applicability of this technique is examined from different perspectives by including various road classes, damage severity and location, and noise. The results prove the robustness and reliability of using VMD for drive-by damage detection. The method outcomes indicate that through the VMD method, cracks with a depth of 10% to 20% of the beam height can be detected even in the case of a rough road profile. A comparison of the results of the VMD and the well-known empirical mode decomposition (EMD) method has also been conducted. This comparison reveals that by implementing the VMD, precise damage locations can be determined, whereas the EMD fails to detect any damage under the conditions considered in this study. The effects of noise and moving vehicle speed are also investigated in the research, and it is found that processing the output signals using VMD can yield reliable estimates of the damage location(s).

Application of Mechano-Luminescence-Optoelectronic Composites for Delamination Detection in Fiber Reinforced Polymer Composites

Abstract Fiber reinforced polymer (FRP) composites have attracted significant attention from the aerospace industry in recent years. FRP composites have the advantage of being light weight, high strength and resistant to corrosion making them an attractive option for various applications in the aerospace industry. However, FRP composites are prone to delamination which can eventually compromise structural integrity leading to sudden failure. An effective damage detection methodology is therefore necessary to monitor delamination in structures. In this study, mechano-luminescence-optoelectronic (MLO) composites were integrated with FRP composites to create MLO-FRP test coupons for delamination detection. The MLO composites were prepared by coupling a Poly(3-hexylthiophene) (P3HT)-based sensor with a copper dopped zinc sulfide (ZnS:Cu) composite. MLO composites were attached to the surface of FRP composites to create MLO-FRP test coupons. The MLO-FRP test coupon was mounted on a 3-point bending load frame and subjected to cyclic loading and unloadings at various displacements with the first displacement starting from zero to 5 mm. The second displacement starting from zero to 6 mm and so on. A direct current (DC) voltage was recorded from the MLO composites at a constant loading rate to validate its DC-based strain sensing capability. Other parameters such as applied load, strain energy release rate and strain were also obtained with the aim of tracking delamination propagation in FRP composites. Results showed MLO composites were sensitive to delamination propagation highlighting their potential to be used in structures for health monitoring.

Machine Learning approaches to damage detection in composite structures combining experimental and simulation domains

Composite materials are widely used across major industries such as the automotive, aerospace and wind power, due to their excellent mechanical properties. A strong effort is thus put into developing innovative damage detection methodologies, for which Non-Destructive Testing (NDT) techniques can play a vital role as advanced measurement methods. One such technique is Laser Doppler Vibrometry which allows to accurately measure high-frequency vibration behavior with dense grids of points, without mass loading the structure. For data analysis, Machine Learning (ML) techniques have achieved high success on a number of structural applications, and can be leveraged to build automated and reliable damage classifiers. In this work, three methodologies have been developed by combining Laser Doppler Vibrometer (LDV) measurements with ML approaches, for the task of detecting damages on a carbon-fiber reinforced polymer (CFRP) plate. Each damage detection methodology requires pre- and post-processing steps, which were optimized with Bayesian Optimization. Principal Component Analysis (PCA) was also explored to reduce the dimensionality of the data, before classification. Moreover, making use of Finite Element Analysis (FEA), simulation data was generated with the ability of characterizing the high-frequency dynamic behavior of ply-based composite plates. The simulation data enriched the damage detection methodologies in a Transfer Learning (TL) framework. Results are presented for each damage detection methodology, alongside with a comparative overview of the advantages and disadvantages of each method.

Towards vibration-based damage detection of civil engineering structures: overview, challenges, and future prospects

AbstractIn this paper, we delve into the evolving landscape of vibration-based structural damage detection (SDD) methodologies, emphasizing the pivotal role civil structures play in society's wellbeing and progress. While the significance of monitoring the resilience, durability, and overall health of these structures remains paramount, the methodology employed is continually evolving. Our focus encompasses not just the transformation brought by the advent of artificial intelligence but also the nuanced challenges and future directions that emerge from this integration. We shed light on the inherent nonlinearities civil engineering structures face, the limitations of current validation metrics, and the conundrums introduced by inverse analysis. Highlighting machine learning's (ML) transformative role, we discuss how techniques such as artificial neural networks and support vector machine's have expanded the SDD's scope. Deep learning's (DL) contributions, especially the innovative capabilities of convolutional neural network in raw data feature extraction, are elaborated upon, juxtaposed with the potential pitfalls, like data overfitting. We propose future avenues for the field, such as blending undamaged real-world data with simulated damage scenarios and a tilt towards unsupervised algorithms. By synthesizing these insights, our review offers an updated perspective on the amalgamation of traditional SDD techniques with ML and DL, underlining their potential in fostering more robust civil infrastructures.

Numerical and Statistical Analysis of the Influence of Damage and Temperature on EMI for Structural Health Monitoring

Structural health monitoring (SHM) is a process of implementing a system to monitor the structural condition of a structure and assess the structural health. SHM is used to detect damage, predict future performance, and monitor the overall structural health of a structure. Electromechanical impedance (EMI)-based SHM is a non-destructive evaluation (NDE) technique that can be used to detect damage in structures by measuring the changes in the electrical impedance of a piezoelectric transducer that is bonded to the structure. EMI has several advantages over other NDE techniques, including being a passive, local, and relatively inexpensive technique. However, one of the challenges of using EMI for structural health monitoring (SHM) is the effect of damage and temperature on the impedance of the piezoelectric transducer. As the damage and temperature changes, the impedance of the transducer will also change. This can make it difficult to detect small damage if the damage and temperature co-exist on the same structure (aluminum plate). This research presents a numerical and statistical study to investigate the effects of damage and temperature on EMI-based SHM. The numerical model uses ANSYS software to simulate an aluminum plate at different temperatures. The findings have shown that both damage and temperature have a significant effect on the impedance of the transducer. However, the study has also demonstrated that it is still possible to detect structural damage using EMI even under varying damage and temperature conditions. This underscores the robustness of EMI-based damage detection methodologies in practical applications.

Time Series Analysis Methodology for Damage Detection in Civil Structures

Structural health monitoring (SHM) methodologies employing data-driven techniques are becoming increasingly popular for detection of structural damage at the earliest stage possible. With measured vibration signals from the structure, time series modeling methods provide quantitative means for extracting such features that can be utilized for damage diagnosis. In this study, one-step prediction error of an autoregressive (AR) model over a data set is used as damage indicator. In particular, the difference between the prediction of the AR model that is fit to the measured acceleration signal obtained from the intact structure and actual measured signals collected for different damage states of the structure are interrogated for diagnosis purposes. More specifically, the standard deviation of the residual error is employed to locate the damaged region. Singular-value decomposition (SVD) is employed to find the optimal order for an AR model created using the impulse responses of the system. Numerical simulations are carried out using the impulse responses acquired from a four-story frame structure contaminated with additive noise including single and multiple damaged elements. The results of the simulations demonstrate that the method can be effectively employed to detect and locate damage. The performance of the proposed procedure are further demonstrated using the impact data acquired from a reinforced concrete frame for real applications.

Improving damage detection by combining multiple classifiers in different feature spaces

Vibration-based damage identification methods have been explored in several engineering fields. However, the effective damage-sensitivity of vibration-based features and the balance between false alarms and readiness of the anomaly detection are still open issues. Here, a cost-effective damage detection methodology based on a Deterministically Generated Negative Selection Algorithm is implemented and tested over a wide dataset generated through the numerical simulation of a three-storey concrete building in different damage scenarios. The approach allows training the classifiers only based on undamaged samples and ensures a prompt response to damage outbreak by fusing the classification conducted in parallel on different feature spaces, exploiting their distinct sensitivity to damage. A strategy to improve the reliability of the alarm is implemented by introducing counters and thresholds on consecutive classification outputs. A satisfactory trade-off between false and true alarms is achieved by comparing their probabilities, obtained through Markov chains, for different values of such thresholds.

Structural damage detection using deep learning and FE model updating techniques

The structural condition can be estimated by various methods. Damage detection, as one of those methods, deals with identifying changes in specific features within structural behavior based on numerical models. Since the method is based on simulation for various damage conditions, there are limitations in applicability due to inevitable discrepancies between the analytical model and the actual structure. Finite element model updating is a technique for establishing a finite element model that can reflect the current state of a target structure based on the measured responses. It is performed based on optimization for various structural parameters, but the final output can converge differently depending on the initial model and the characteristics of the algorithm. Although the updated model may not faithfully replicate the target structure as it is, it can be considered equivalent in terms of the relationship between the structural properties and behavioral characteristics of the target. This allows for the analysis of changes in the mechanical relationships established for the target structure. The change can be related to structural damage, and artificial intelligence technology can provide an alternative solution in such complex problems where analytical approaches are challenging. Taking practical aspects from the aforementioned methods, a novel structural damage detection methodology is presented in this study for identifying the location and extent of the damage. Model updating is used to establish a reference model that reflects the structural characteristics of the target. Training data for various damage conditions based on the reference model allows the artificial intelligence networks to identify damage to the target structure.

Intelligent Fault Diagnosis of Rolling Bearings Based on a Complete Frequency Range Feature Extraction and Combined Feature Selection Methodology

The utilization of multiscale entropy methods to characterize vibration signals has proven to be promising in intelligent diagnosis of mechanical equipment. However, in the current multiscale entropy methods, only the information in the low-frequency range is utilized and the information in the high-frequency range is discarded. In order to take full advantage of the information, in this paper, a fault feature extraction method utilizing the bidirectional composite coarse-graining process with fuzzy dispersion entropy is proposed. To avoid the redundancy of the full frequency range feature information, the Random Forest algorithm combined with the Maximum Relevance Minimum Redundancy algorithm is applied to feature selection. Together with the K-nearest neighbor classifier, a rolling bearing intelligent diagnosis framework is constructed. The effectiveness of the proposed framework is evaluated by a numerical simulation and two experimental examples. The validation results demonstrate that the extracted features by the proposed method are highly sensitive to the bearing health conditions compared with hierarchical fuzzy dispersion entropy, composite multiscale fuzzy dispersion entropy, multiscale fuzzy dispersion entropy, multiscale dispersion entropy, multiscale permutation entropy, and multiscale sample entropy. In addition, the proposed method is able to identify the fault categories and health states of rolling bearings simultaneously. The proposed damage detection methodology provides a new and better framework for intelligent fault diagnosis of rolling bearings in rotating machinery.

Vibration-based hidden damage imaging using stereo cameras with digital image correlation

This paper explores a full-field non-contact optical sensing technique using a stereo camera for imaging hidden damage based on vibration-based damage detection methodology in structural health monitoring. The technique utilizes a pair of digital cameras to capture dynamic operational deflection shapes (ODSs) over the region of interest (ROI) of a structure’s surface via digital image correlation (DIC) when subjected to vibrational excitation. This research overcomes bottlenecks in using high vibration modes for imaging the hidden damage area by (1) applying DIC to operational modal analysis with simple pick-peaking techniques to gather natural frequencies and operational mode shapes in plate structures, while (2) using wavelet analysis to reveal the image of the damage region as a means for baseline-free global damage quantification. In the feasibility study, four cases with two aluminum plates with large damage regions were investigated with a vibration shaker generating a frequency sweep up to 1 kHz. The stereo camera imaged the speckled surface of the plate with white light. Once the dynamic ODSs in the ROI were observed using DIC, the natural frequencies and associated operational mode shapes were extracted using a peak-picking technique in the frequency spectrum. Natural frequencies and operational mode shapes from finite element analysis correlated well with the experimental observations from three-dimensional DIC for all 12 vibration modes respectively. A wavelet transform mode shape curvature (WT-MSC) technique to obtain the modal shape curvature via a two-dimensional continuous wavelet transform with a Mexican Hat analyzing wavelet was then implemented on each of the first 12 vibration mode shapes. A damage image condition that incorporates all weighted wavelet coefficients is proposed to image the damage region. The hidden damage was visualized clearly with WT-MSC, as the technique is much less sensitive to noise than the use of MSC alone, and the use of high vibration modes exhibiting larger mode shape curvatures provided a greater sensitivity for imaging the damage region. Hidden damage regions were successfully visualized in all four cases.

Experimental characterization of impact damage in foam-core sandwich structures using acoustic emission, optical scanning and X-ray computed tomography techniques

Sandwich structures used in transportation and energy industries are commonly subjected to low-velocity impact in the course of manufacturing, service and maintenance, which can lead to the premature degradation of structural stiffness and strength. A single non-destructive evaluation technique is often difficult to provide a comprehensive assessment on impact damage from online to offline and from visible to invisible. In the present study, acoustic emission (AE), three-dimensional (3D) optical scanning and X-ray computed tomography (CT) techniques are combined to provide a systematic approach for damage characterization of thick foam-core sandwich panels under low-velocity impact. In the tests, the mechanical responses such as force-time and force-displacement curves are obtained from the testing system. The AE technique is adopted to monitor damage initiation and evolution in situ; optical scanning and X-ray computed tomography techniques are used for off-line damage identification and quantification. The results show that the AE signal waveform stream, cumulative counts and cumulative energy correlate well with the mechanical responses of the sandwich panel under the low-velocity impact, enabling capture of valuable information for online damage monitoring. Unlike general cases, the AE signals generated from low-velocity impact are found to be presented in strong bursts of nearly coincident events in the short duration. Therefore, the distributions of AE amplitude and peak frequency between adjacent AE counts are extracted to study the different damage modes and damage mechanisms. The X-ray computed tomography images enable to precisely visualize the fracture behaviors of the PET foam-core inside the sandwich panel in a nondestructive fashion, which evidently supports the AE and optical scanning results. The study is anticipated to demonstrate effectiveness and capability of the proposed experimental methodology for damage detection and characterization on sandwiched composite structures subjected to impact loading.

Wind turbine blade damage detection using data-driven techniques

This work presents a simple damage detection strategy for wind turbine blades. In particular, a vibration analysis-based damage detection methodology is proposed that requires only healthy data and detects damage in different locations of the blade. The stated structural health monitoring strategy is based on the extraction of characteristics using statistical metrics as a technique for the recognition and differentiation of healthy test experiments from damaged test experiments with simulated faults created by added mass. In this manner, several metrics are approached to find those that show better classification in processing the data provided by the sensors. Finally, an evaluation process is performed to detect blade damage. The results show that the proposed RMSE metric performs at an ideal level, making it a promising strategy for the detection of blade damage.

AI-based condition monitoring on mechanical systems using multibody dynamics models

Real-time monitoring of mechanical systems via vibration measurements allows for detection of faults in them and facilitates their predictive maintenance. Use of Artificial Intelligence (AI) in damage detection can provide an automated means for Condition Monitoring (CM) of the system while also allowing for characterization of the various health states when labeled damaged state training datasets are available. In this work, a novel CM framework using Convolutional Neural Networks (CNNs) is presented for damage detection and identification on mechanical systems, applied on an elevator door rail, using simulated data from a Multibody Dynamics (MBD) model of the physical system. First, an optimal MBD model of the actual structure is constructed by means of black box optimization, using a small number of initial healthy state measurements. Several of the most common nonlinear contact force models are examined before integrating the best suited one to the MBD model. Damaged states of the system are then simulated by modeling various fault mechanisms on the optimal MBD model, allowing for generation of labeled simulated training datasets for the CNNs. Uncertainty is introduced to the models by sampling their key parameters from a Gaussian distribution, thus considering the real system’s inherent uncertainty. The trained CNNs’ robustness and accuracy are then validated by accurately classifying faults on the physical system, proving the proposed damage detection method’s generalization capabilities and highlighting its potential. The proposed methodology may find application in mitigating the problem of data scarcity of damaged state responses which is present in most mechanical systems and can provide a means for more efficient predictive maintenance. Successful implementation of the proposed framework adds to the existing AI based damage detection and identification methodologies by allowing for extension of the method to industrial systems.

Damage Detection of a Pressure Vessel with Smart Sensing and Deep Learning

Structural Health Monitoring plays a crucial role in ensuring the safety and reliability of critical infrastructure, including pressure vessels involved in various applications. This research reports the damage detection of a pressure box employed in space habitat that operates in harsh environment where both structural failure and bolt joint loosening may occur. These failure modes are extremely hard to model based on first principles. We explore proper sensing mechanism and the associated inverse analysis algorithm that can elucidate the health condition of the pressure box. It is identified that piezoelectric impedance based active interrogation can provide necessary information for damage detection in such a system. Concurrently, deep learning technique leveraging spatial convolutional neural network is synthesized to analyze the raw data acquired and identify different types of damage. By training the deep learning model on a dataset of healthy and various damage scenarios, we can achieve high accuracy in identifying the presence of damage and its type. This research provides a data-driven methodology for structural damage detection using deep learning and has the potential to be extended to various systems with different failure modes.

Feasibility of Applying Mel-Frequency Cepstral Coefficients in a Drive-by Damage Detection Methodology for High-Speed Railway Bridges

In this paper, a drive-by damage detection methodology for high-speed railway (HSR) bridges is addressed, to appraise the application of Mel-frequency cepstral coefficients (MFCC) to extract the Damage Index (DI). A finite element (FEM) 2D VTBI model that incorporates the train, ballasted track and bridge behavior is presented. The formulation includes track irregularities and a damaged condition induced in a specified structure region. The feasibility of applying cepstrum analysis components to the indirect damage detection in HSR by on-board sensors is evaluated by numerical simulations, in which dynamic analyses are performed through a code implemented in MATLAB. Different damage scenarios are simulated, as well as external excitations such as measurement noises and different levels of track irregularities. The results show that MFCC-based DI are highly sensitive regarding damage detection, and robust to the noise. Bridge stiffness can be recognized satisfactorily at high speeds and under different levels of track irregularities. Moreover, the magnitude of DI extracted from MFCC is related to the relative severity of the damage. The results presented in this study should be seen as a first attempt to link cepstrum-based features in an HSR drive-by damage detection approach.

Beam-like damage detection methodology using wavelet damage ratio and additional roving mass

Early damage detection plays an essential role in the safe and satisfactory maintenance of structures. This work investigates techniques use only damaged structure responses. A Timoshenko beam was modeled in finite element method, and an additional mass was applied along their length. Thus, a frequency-shift curve is observed, and different damage identification techniques were used, such as the discrete wavelet transform and the derivatives of the frequency-shift curve. A new index called wavelet damage ratio(WDR) is defined as a metric to measure the damage levels. Damages were simulated like a mass discontinuity and a rotational spring (stiffness damage). Both models were compared to experimental tests since the mass added to the structure is a non-destructive tool. It was evaluated different damage levels and positions. Numerical results showed that all proposed techniques are efficient techniques for damage identification in Timoshenko's beams concerning low computational cost and practical application.