Search Results (309)

Analyzing and measuring the similarity between two signals is a common task in many vibration-based structural health monitoring applications. Coherence between input and response signals serves as a convenient indicator of damage, based on the premise that nonlinearity due to damage in a linear system manifests as a loss of coherence in specific frequency bands. Because input excitations in civil structures are difficult to measure, damage indicators based on the coherence between two response signals have been developed. These indicators have shown promise in detecting nonlinear behavior in structures that were initially linear. This paper proposes a new damage indicator based on Mutual Information, a nonlinear extension of the squared correlation coefficient, to quantify the similarity between two signals without making assumptions about the nature of their interactions or the underlying dynamics of the system. Mutual Information is distinguished from other nonlinear similarity metrics due to its ability to capture all types of nonlinear dependencies, its high computational efficiency, and its invariance to invertible transformations, such as scaling. The proposed approach is demonstrated using a standard dataset containing experimental data from a three-story aluminum frame structure under 17 different damage states. The results show that the proposed metric can detect deviations from the baseline state due to changes in mass, stiffness, or newly induced nonlinear behavior, suggesting its potential for monitoring changes in the structural system. Full article

Corrosion is a critical issue in civil structures, significantly affecting their durability and functionality. Detecting corrosion at an early stage is essential to prevent structural failures and ensure safety. This study proposes an expert system based on a novel methodology for corrosion detection using vibration signal analysis. The approach employs graphical empirical mode decomposition (GEMD) to decompose vibration signals into their intrinsic mode functions, extracting relevant structural features. These features are then transformed into grayscale images and classified using a Convolutional Neural Network (CNN) to automatically differentiate between a healthy structure and one affected by corrosion. To enhance the computational efficiency of the method without compromising accuracy, different CNN architectures and image sizes are tested to propose a low-complexity model. The proposed approach is validated using a 3D nine-bay truss-type bridge model encountered in the Vibrations Laboratory at the Autonomous University of Querétaro, Mexico. The evaluation considers three different corrosion levels: (1) incipient, (2) moderate, and (3) severe, along with a healthy condition. The combination of GEMD and CNN provides a highly accurate corrosion detection framework that achieves 100% classification accuracy while remaining effective regardless of the damage location and severity, making it a reliable tool for early-stage corrosion assessment that enables timely maintenance and enhances structural health monitoring to improve the long life and safety of civil structures. Full article

Recent advances in sensor technology, data acquisition, and signal processing have enabled the development of data-driven structural health monitoring (SHM) strategies, offering a powerful alternative or complement to traditional model-based approaches. These approaches rely on damage-sensitive features (DSFs) extracted from vibration measurements. This study introduces an innovative, unsupervised learning framework leveraging transmissibility functions (TFs) as DSFs due to their local sensitivity to changes in dynamic behavior and their ability to operate without requiring input excitation measurements—an advantage in civil engineering applications where such data are often difficult to obtain. The novelty lies in the use of sequential sensor pairings based on structural connectivity to construct TFs that maximize damage sensitivity, combined with one-class classification algorithms for automatic damage detection and a damage index for spatial localization within sensor resolution. The method is evaluated through numerical simulations with noise-contaminated data and experimental tests on a masonry arch bridge model subjected to progressive damage. The numerical study shows detection accuracy above 90% with one-class support vector machine (OCSVM) and correct localization across all damage scenarios. Experimental findings further confirm the proposed approach’s localization capability, especially as damage severity increases, aligning well with observed damage progression. These results demonstrate the method’s practical potential for real-world SHM applications. Full article

The big data collected from structural health monitoring systems (SHMs), combined with the rapid advances in machine learning (ML), have enabled data-driven methods in practical SHM applications. These methods typically use ML algorithms to identify patterns within features extracted from data representing structural conditions, thereby inferring damage from changes in these patterns. However, data-driven models often struggle to generalize effectively to unseen datasets. This study addresses this challenge through three key contributions: dataset augmentation, an efficient feature representation, and a probabilistic modeling approach. First, a data augmentation method leveraging the symmetric properties of bridge structures is introduced to enhance dataset diversity. Second, a novel damage indicator named Fre-GraRMSC1 is proposed, capable of distinguishing both damage locations and severity. Finally, a probabilistic generative model based on a deep belief network (DBN) is developed to predict damage locations and degrees. The proposed methods are validated using vibration data from a numerical three-span continuous bridge subjected to random vehicle excitations. Results demonstrate high accuracy in damage identification and improved generalization performance. Full article

The increasing demands for the reliability of modern industrial equipment and structures necessitate advanced techniques for design, monitoring, and analysis. This review article presents the latest research advancements in the application of machine learning techniques to vibration and acoustic signal analysis from 2015 to 2024. A total of 96 peer-reviewed scientific publications were examined, selected using a systematic Scopus-based search. The main research areas include processes such as modeling and design, health management, condition monitoring, non-destructive testing, damage detection, and diagnostics. In the context of these processes, a review of machine learning techniques was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), autoencoders, support vector machines (SVMs), decision trees (DTs), nearest neighbor search (NNS), K-means clustering, and random forests. These techniques were applied across a wide range of engineering domains, including civil infrastructure, transportation systems, energy installations, and rotating machinery. Additionally, this article analyzes contributions from different countries, highlighting temporal and methodological trends in this field. The findings indicate a clear shift towards deep learning-based methods and multisensor data fusion, accompanied by increasing use of automatic feature extraction and interest in transfer learning, few-shot learning, and unsupervised approaches. This review aims to provide a comprehensive understanding of the current state and future directions of machine learning applications in vibration and acoustics, outlining the field’s evolution and identifying its key research challenges and innovation trajectories. Full article

Two different magnetoelastic Metglas materials with distinct shapes were compared as sensing elements for the structural health monitoring of concrete beams. One had a ribbon shape, while the other had a microwire shape. The sensing elements were attached to different concrete beams, and a crack was introduced into each beam. The beams were subjected to flexural vibrations, and their deformations were recorded wirelessly by coils, detecting the magnetic signals emitted due to the magnetoelastic nature of the sensors. Fast Fourier Analysis of the received signal revealed the bending mode frequencies of the beams, which serve as a “signature” of their structural health. In these spectra, the ribbon-shaped sensor exhibited a 1.4-times stronger signal than the microwire sensor. However, the extracted mode frequencies were nearly identical, with differences of less than 1% both before and after damage. This indicates that both sensors can be used equivalently to monitor structural damage in concrete beams. The damage-related relative frequency shifts ranged from −0.01 to −0.03, with similar results for both sensors. Thermal annealing was also studied and appeared to significantly enhance the signal by 10–30%, likely due to the relaxation of internal stresses induced during the rapid solidification synthesis of these materials. This enhancement was more pronounced in the ribbon-shaped sensor. This study is the first to utilize a magnetoelastic microwire sensor for damage detection in concrete beams. Full article

In this study, we propose a novel approach for dynamic micro-vibration measurement based on an interferometric system utilizing a fractional optical vortex (FOV) beam as the reference and a Gaussian beam as the measurement path. The reflected Gaussian beam encodes the vibration information of the target, which is extracted by analyzing the rotational behavior of the petal-like interference pattern formed through coaxial interference with the FOV beam. When the topological charge (TC) of the FOV beam is less than or equal to one, a single-petal structure is generated, significantly reducing the complexity of angular tracking compared to traditional multi-petals OAM-based methods. Moreover, using a Gaussian beam as the measurement path mitigates spatial distortions during propagation, enhancing the overall robustness and accuracy. We systematically investigate the effects of TC, CCD frame rate, and interference contrast on measurement performance. Experimental results demonstrate that the proposed method achieves high angular resolution with a minimum angle deviation of 18.2 nm under optimal TC conditions. The system exhibits strong tolerance to environmental disturbances, making it well-suited for applications requiring non-contact, nanometer-scale vibration sensing, such as structural health monitoring, precision metrology, and advanced optical diagnostics. Full article

This paper presents advancements in structural health monitoring (SHM) techniques, with a particular focus on wave-screening methods for assessing prestress loss in a large-scale prestressed concrete (PC) bridge model under outdoor conditions. The wave-screening process utilizes low-frequency wave propagation obtained from seismic interferometry of structural free vibrations and high-frequency wave propagation obtained through ultrasonic transducers embedded in the structure. An adjustable post-tensioning system was employed in a series of experiments to simulate prestress loss. By comparing bridge vibrations under varying post-tensioning forces, the study investigated prestress loss and examined temperature-related effects using the coda wave interferometry (CWI) method. Local structural alterations were analyzed through wave velocity variations, demonstrating sensitivity to bridge temperature changes. The findings indicate that wave-based methods are more effective than traditional modal analysis for damage detection, highlighting the dual impacts of prestress loss and temperature, as well as damage localization. This study underscores the need for long-term measurements to account for temperature fluctuations when analyzing vibration measurements to investigate changes in prestressing force in PC structures. Full article

Monitoring gearbox health is essential in industrial systems, where undetected faults can result in costly downtime and severe equipment damage. While vibration-based diagnostics are widely utilized for fault detection, analyzing large-scale, non-stationary vibration signals remains a computational challenge, particularly in real-time and resource-constrained environments. This paper presents Data-Driven Synchrosqueezing-based Signal Transformation (DSST), a novel time-frequency method that integrates synchrosqueezing transform (SST) with structured downsampling in both time and frequency domains. DSST significantly reduces computational and memory demands, while preserving high-resolution representations of fault-related features such as gear meshing frequency sidebands and their harmonics. In contrast to prior SST variants, DSST emphasizes diagnostic interpretability, invertibility, and compatibility with data-driven learning models, making it suitable for deployment in modern condition monitoring frameworks. Experimental results on non-stationary gearbox vibration data demonstrate that DSST achieves comparable diagnostic accuracy to conventional SST methods, with substantial gains in processing efficiency—thereby supporting scalable, real-time industrial health monitoring. Unlike existing downsampling-based SST methods, DSST is designed as a diagnostic component within a scalable, data-driven framework, supporting real-time analysis, signal reconstruction, and downstream machine learning integration. Full article

In the fields of fault diagnosis and structural health monitoring using sound and vibration, there is increasing interest in data compression techniques based on Compressed Sensing (CS). However, conventional CS approaches that use standard bases such as Fourier or wavelets are unable to achieve sparse representations of operational vibrations in rotating machinery with speed variations, leading to significantly reduced compression performance. To overcome this limitation, this study introduces a CS approach that incorporates order analysis, a technique commonly used in the analysis of rotating machinery. The method constructs an order basis using randomly sampled rotational speed data, enabling sparse observation of operational vibrations through CS. This represents a novel approach for efficiently capturing the essential features of vibration signals under rotational speed variations. The proposed method was validated through numerical experiments. The results showed that for rotational vibrations with speed variations of approximately 10% of the average speed, the compression performance was 20 times higher than that of conventional methods using the Fourier basis. Furthermore, evaluations using simulated vibration signals from eccentric faulty gears, as well as experimental data from defective propellers and bearings with outer ring defects, demonstrated that the proposed method could successfully reconstruct signals even under conditions with substantial speed variation—conditions under which conventional Fourier-based methods fail. Due to its superior compression performance and its ability to handle unknown operational vibrations, the proposed method is highly suitable for applications in fault diagnosis, structural health monitoring, and vibration measurement. Full article

This paper focuses on the mathematical and numerical modeling of a non-classical macro fiber composite (MFC) piezoelectric transducer, MFC-P2, integrated with an aluminum cantilever beam for energy harvesting applications. It seeks to harness the transverse vibration energy in the environment to power small electronic devices, such as wireless sensors, where conventional power sources are inconvenient. The P2-type macro fiber composites (MFC-P2) are specifically designed for transverse energy harvesting applications. They offer high electric source capacitance and improved electric charge generation due to the strain developed perpendicularly to the voltage produced. The system is modeled analytically using Euler–Bernoulli beam theory and piezoelectric constitutive equations, capturing the electromechanical coupling in the d31 mode. Numerical simulations are conducted using COMSOL Multiphysics 6.29 to reduce the complexity of the mathematical model and analyze the effects of material properties, geometric configurations, and excitation conditions. The theoretical model is based on the transverse vibrations of a cantilevered beam using Euler–Bernoulli theory. The natural frequencies and mode shapes for the first four are determined. Depending on these, the resonance frequency, voltage, and power outputs are evaluated across a 12 kΩ resistive load. The results demonstrate that the energy harvester effectively operates near its fundamental resonant frequency of 10.78 Hz, achieving the highest output voltage of approximately 0.1952 V and a maximum power output of 0.0031 mW. The generated power is sufficient to drive ultra-low-power devices, validating the viability of MFC-based cantilever structures for autonomous energy harvesting systems. The application of piezoelectric phenomena and obtaining electrical energy from mechanical vibrations can be powerful solutions in such systems. The application of piezoelectric phenomena to convert mechanical vibrations into electrical energy presents a promising solution for self-powered mechatronic systems, enabling energy autonomy in embedded sensors, as well as being used for structural health monitoring applications. Full article

The accuracy of identifying dynamic characteristics of super-tall buildings under typhoon conditions, as well as their correlation with the vibration amplitude, remains unclear, limiting the effective assessment of the structural performance and optimization of wind-resistant designs. To address this issue, the measured wind-generated vibration responses of Shanghai World Finance Center during the passage of Typhoon “Rumbia” were derived using data obtained from the health monitoring system of a super-tall building in Shanghai. The first and second inherent frequencies, as well as the damping ratio of the structure, were ascertained through the employment of the curve method and the standard deviation method. Based on this, a comparison and analysis were carried out regarding the variation patterns of the first and second inherent frequencies and the damping ratio with reference to the vibration amplitude. Vibration modes were identified using frequency domain analysis. The results of the natural frequency identification were compared to those from the Peak Picking method to see how well the curve method and the standard deviation method worked at finding modal parameters. Ultimately, an assessment of the super-tall building’s performance during the impact of the typhoon was conducted. The results demonstrate that the curve method and the standard deviation method can accurately identify the inherent frequency and damping ratio of the structure, with the curve method revealing a more pronounced regularity of the modal parameters. For the structure, in the horizontal and longitudinal directions, the first and second inherent frequencies exhibit a negative correlation with amplitude, while the damping ratio shows a positive correlation with amplitude. Moreover, as the floor level rises, the vibration modes in both directions of the structure steadily increase. During the impact of Typhoon “Rumbia”, the building’s performance complied with the requirements set by comfort standards. These analytical results not only provide valuable references for the wind-resistant design and vibration control of super-tall buildings but also offer critical support for condition assessment and damage identification within structural health monitoring systems. Full article

Structural health monitoring (SHM) systems are very useful for evaluating the performance of bridges in service. In this paper, the SHM system implemented on the Poyang Lake Second Bridge is investigated, and the monitored data are analyzed for performance evaluation, damage identification, and model updating of the bridge. First, the measured data are examined for environmental effects, structural behaviour, and modal identification. Based on the bridge construction information, a finite element (FE) model is constructed for the cable-stayed bridge. Subsequently, the regularized model updating approach is employed to calibrate the constructed numerical model by using the measured modal data. Several vibration-based methods for structural damage identification are proposed to inversely identify the simulated damage within the cable-stayed bridge using the test data. The results indicate that the measured structural responses, such as cable forces and bridge deck deflections, vary over time and highlight discrepancies in the initial FE model. This FE numerical model can then be effectively adjusted using the proposed model updating method, which enhances the connection between the real cable-stayed bridge and the modified FE numerical model. From the modal data, the simulated damage in the main structural members of the cable-stayed bridge can be correctly identified using the proposed methods. Full article

The growing vulnerability of residential buildings to seismic activity and dynamic loading underscores the need for robust, real-time Structural Health Monitoring (SHM) systems. This study investigates the feasibility of utilizing piezoelectric sensors integrated with advanced signal processing techniques, including Power Spectral Density (PSD) and Short-Time Fourier Transform (STFT), for vibration-based SHM in residential structures. A scaled three-story building prototype was fabricated and subjected to controlled base excitations at 25 Hz and 0.6 g acceleration to evaluate the response under three structural conditions: healthy, randomly damaged, and thickness-damaged. The experimental results revealed that structural degradation significantly altered sensor outputs, with random damage causing irregular signal dispersion and thickness damage introducing additional frequency harmonics. PSD analysis effectively identified shifts in energy distribution, signifying structural degradation, while STFT provided a detailed time-frequency representation, facilitating real-time damage detection. The findings confirm that piezoelectric sensors, when combined with PSD and STFT, can serve as a low-cost, scalable solution for early damage detection in residential buildings, offering a practical framework for enhancing structural resilience against earthquakes and other dynamic forces. Full article

Health indicators (HIs) are quantitative indices that assess the condition of engineering systems by linking sensor data with monitoring, diagnostic, and prognostic methods to estimate the remaining useful life (RUL). Digital twins (DTs), which serve as digital representations of physical assets, enhance system monitoring, diagnostics, and prognostics by operationalizing analytic capabilities derived from sensor data. This paper explores the integration of HIs and DTs, illustrating their roles in condition-based maintenance and structural health monitoring. The methodologies discussed span data-driven and physics-based approaches, emphasizing their applications in rotary machinery, including bearings and gears. These approaches not only detect anomalies but also predict system failures through advanced modeling and machine learning (ML) techniques. The paper provides examples of HIs derived from vibration analysis and soft sensors and maps future research directions for improving health monitoring systems through hybrid modeling and uncertainty quantification. It concludes by addressing the challenges of data labeling and uncertainties and the role of HIs in advancing performance engineering, making DTs a pivotal tool in predictive maintenance strategies. Full article