Search Results (103)

Structural health monitoring (SHM) is a component of modern civil engineering. This review analyzes the integration of sensing technologies and artificial-intelligence-based methods for damage detection, localization, classification, prognosis, and anomaly detection in buildings and civil infrastructure. The database search covered Web of Science (WoS), Scopus, and IEEE Xplore for the period 1 January 2020–31 December 2025. The initial records were 292 in WoS, 311 in Scopus, and 338 in IEEE Xplore; after applying the AI-related search constraint, the corresponding AI-SHM corpora were 71, 79, and 139 records, respectively. The combined screening and eligibility workflow produced 31 open-access studies for detailed qualitative analysis, while the task-specific performance tables synthesize the subset of studies for which the sensor type, AI model, SHM task, validation context, and performance metrics were explicitly reported. The review, therefore, interprets reported performance by SHM task and sensor modality, rather than treating heterogeneous metrics as directly comparable across different datasets and experimental conditions. The results indicate that high values reported for accelerometer-, fiber-optic-, piezoelectric transducer-, and vision-based systems are mainly obtained under controlled, benchmark, simulated, or study-specific validation conditions. Consequently, robustness, transferability to operational structures, uncertainty quantification, sensor-network design, and integration with Physics-Informed Machine Learning and Digital Twin technologies remain central research needs. Full article

In this work, a novel approach for implementing a resonant micro-accelerometer is demonstrated that may extend the operating frequency of such devices to several tens of MHz, which may enable direct wireless signal transfer. The proposed resonant accelerometer consists of a hybrid structure: a piezoelectric micro-resonator and a capacitive mass-spring (CMS) system (that are mechanically separated but electrically interconnected). The sensor utilizes the piezoelectric stiffening mechanism, which translates the acceleration-induced displacement of the capacitive mass-spring (CMS) structure into a shift in the resonance frequency of the interconnected resonator. The operating principle is elaborated upon in detail, supported by simulation and experimental results. Additionally, a novel fabrication technique is presented to realize a suspended fixed bi-layer electrode for the CMS in which a hardened layer of photoresist is utilized as a sacrificial layer. The experimental sensitivity of a fully functional device is reported to be ~6 Hz/g at 25 MHz (~0.23 ppm/g), which closely matches the simulated sensitivity of ~7 Hz/g (~0.278 ppm/g) for the fabricated capacitive gap of ~7 µm. Full article

Long-distance coal gangue slurry transportation pipelines are critical components of underground coal mine green backfilling systems, yet blockage failures severely threaten their safe and efficient operation. Existing distributed acoustic sensing (DAS)-based monitoring methods for such pipelines suffer from three key limitations: insufficient fixed-point quantitative accuracy, lack of verified blockage-specific characteristic indicators, and limited quantitative severity assessment capability. To address these gaps, this paper proposes a novel feature-level fusion monitoring method integrating DAS, fiber Bragg grating (FBG), and piezoelectric accelerometers for accurate blockage identification and quantitative evaluation in coal gangue slurry pipelines. A slurry pipeline circulation test platform with gradient blockage simulation (0% to 76.42%) and a synchronous multi-sensor monitoring system were developed. Through multi-domain signal analysis, three blockage-correlated characteristic frequencies were identified and cross-validated by synchronous multi-sensor data: 1.5 Hz (system background vibration), 26 Hz (blockage-induced fluid–structure resonance, verified by the Euler–Bernoulli beam theory with a theoretical value of 25.7 Hz), and 174 Hz (transient flow impact). The DAS phase change rate exhibited a unimodal nonlinear response to blockage degree, with the peak occurring at 40.94% blockage. On this basis, a sine-fitting quantitative inversion model was developed, achieving a high goodness of fit (R² = 0.985), and leave-one-out cross-validation confirmed its excellent robustness with a mean relative prediction error of 3.77%. Finally, a collaborative monitoring framework was built to fully leverage the complementary advantages of each sensor, realizing full-process blockage monitoring covering global blockage localization, precise quantitative severity calibration, and high-frequency transient risk early warning. The proposed method provides a robust experimental and technical foundation for real-time early warning, precise localization, and quantitative diagnosis of long-distance slurry pipeline blockages and holds important engineering application value for the safe and efficient operation of underground coal mine green backfilling systems. Full article

The evolution of next-generation urban infrastructure necessitates the deployment of intelligent pavement systems capable of real-time data acquisition, adaptive response, and predictive analytics. This article presents the design, implementation, and performance evaluation of the smart pavement system incorporating multimodal embedded sensors for traffic density analysis, structural health monitoring, and environmental surveillance. SPS integrates piezoelectric transducers, micro-electro-mechanical system accelerometers, inductive loop coils, fiber Bragg grating (FBG) sensors, and capacitive moisture and temperature sensors within the asphalt and sub-base layers, forming a distributed sensor network that interfaces with an edge-AI-enabled data acquisition and control module. Each sensor node performs localized pre-processing using low-power microcontrollers and transmits spatiotemporal data to a centralized IoT gateway over an adaptive mesh topology via long-range wide-area network or 5G-Vehicle-to-Everything protocols. Data fusion algorithms employing Kalman filters, sensor drift compensation models, and deep convolutional recurrent neural networks enable accurate classification of vehicular loads, traffic, and anomaly detection. Additionally, the system supports real-time air pollutant detection (e.g., NO₂, CO, and PM2.5) using embedded electrochemical and optical gas sensors linked to mobile roadside units. Field deployments on a 1.2 km highway testbed demonstrate the system’s capability to achieve 95.7% classification accuracy for vehicle type recognition, ±1.5 mm resolution in rut depth measurement, and ±0.2 °C thermal sensitivity across dynamic weather conditions. Predictive analytics driven by long short-term memory networks yield a 21.4% improvement in maintenance planning accuracy, significantly reducing unplanned downtimes and repair costs. The architecture also supports vehicle-to-infrastructure feedback loops for adaptive traffic signal control and incident response. The proposed SPS architecture demonstrates a scalable and resilient framework for cyber-physical infrastructure, paving the way for smart cities that are responsive, efficient, and sustainable. Full article

This paper presents a systematic review conducted under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, analyzing 286 scientific articles focused on vibration-based predictive maintenance strategies for wind turbines within the context of advanced Prognostics and Health Management (PHM). The review combines international standards (ISO 10816, ISO 13373, and IEC 61400) with recent developments in sensing technologies, including piezoelectric accelerometers, microelectromechanical systems (MEMS), and fiber Bragg grating (FBG) sensors. Classical signal processing techniques, such as the Fast Fourier Transform (FFT) and wavelet-based methods, are identified as key preprocessing tools for feature extraction prior to the application of machine-learning-based diagnostic algorithms. Special emphasis is placed on machine learning and deep learning techniques, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and autoencoders, as well as on hybrid digital twin architectures that enable accurate Remaining Useful Life (RUL) estimation and support autonomous decision-making processes. The bibliometric and case study analysis covering the period 2020–2025 reveals a strong shift toward multisource data fusion—integrating vibration, acoustic, temperature, and Supervisory Control and Data Acquisition (SCADA) data—and the adoption of cloud-based platforms for real-time monitoring, particularly in offshore wind farms where physical accessibility is constrained. The results indicate that vibration-based predictive maintenance strategies can reduce operation and maintenance costs by more than 20%, extend component service life by up to threefold, and achieve turbine availability levels between 95% and 98%. These outcomes confirm that vibration-driven PHM frameworks represent a fundamental pillar for the development of smart, sustainable, and resilient next-generation wind energy systems. Full article

This paper establishes an integrated framework combining self-induced vibration measurements with deep learning for vibration-based remaining useful life (RUL) prediction of mechanical frame structures in mobile robots. The main innovations comprise (1) a self-induced vibration excitation system that utilizes the robot’s drive wheels to generate controlled mechanical oscillations, using a five-sensor micro-electro-mechanical system (MEMS) accelerometer array to capture non-uniform vibration mode shapes across the robot’s structure, and (2) a processing pipeline for RUL prediction using accelerometer data and early feature fusion in two machine-learning models (long short-term memory (LSTM) and a convolutional neural network (CNN)). Our research methodology includes (i) modal analysis to identify the robot’s natural frequencies, (ii) verification platform evaluation, comparing low-cost MEMS accelerometers against a reference integrated electronic piezoelectric (IEPE) accelerometer, demonstrating industrial-grade measurement quality (coherence > 98%, uncertainty 4.79–7.21%), and (iii) data-driven validation using real data from the mechanical frame, showing that the LSTM model outperforms the CNN with a 2.61× root-mean-square error (RMSE) improvement (R² = 0.99). Our solution demonstrates that early feature fusion provides sufficient information to model degradation and detect faults early at a lower cost, offering a feasible alternative to classical maintenance procedures through combined hardware validation and lightweight software suitable for Industrial Internet-of-Things (IIoT) deployment. Full article

Incorporating sensors such as microelectromechanical system (MEMS)-based inertial measurement units (IMUs) and strain gauges into aircraft structures has the potential to complement ground vibration testing results and improve the tracking of structural modes and wing shape in flight, as well as structural health monitoring. This study evaluates the feasibility and accuracy of employing MEMS accelerometers and gyroscopes together with strain gauges to estimate the structural modes of an aircraft. For this purpose, a ground vibration test was carried out on a 1:3 scaled Diana 2 glider model from which the displacement, rotation, and strain modes were estimated. The estimated modal parameters were compared with traditional piezoelectric accelerometer results and Finite Element Method model predictions. The results showed that the modal frequencies, damping ratios, and mode shapes estimated using MEMS IMUs and strain gauges closely matched the reference accelerometer estimates. Furthermore, the combination of displacement, rotation, and strain mode shapes allowed for greater insight into the structural dynamics. The exploratory use of gyroscopes for aircraft GVT allowed the structural torsion to be captured directly, thereby potentially simplifying future GVT setups by eliminating the need for placing accelerometers in pairs across the structure. Full article

Seismic metamaterial-inspired periodic foundations have emerged as promising vibration-mitigation concepts capable of attenuating seismic wave propagation within specific frequency bands. This study presents an experimental investigation on the dynamic response of periodic foundation configurations, with and without embedded piezoelectric layers, to evaluate their vibration-attenuation characteristics. The experimental program employed a shake table driven by a 0.75 kW servo motor and included excitation step counts of 3000, 4000, and 5000. Accelerometers mounted on the specimen surfaces recorded vibration data at 80 ms intervals. Three foundation configurations were tested: (i) a conventional reinforced concrete block, (ii) a one-dimensional periodic foundation composed of alternating concrete and rubber layers, and (iii) a periodic foundation incorporating piezoelectric modules. Time-domain and frequency-domain analyses showed that the periodic foundations achieved notable reductions in both peak and RMS accelerations, especially near resonance frequencies. The configuration, including piezoelectric layers, exhibited similar attenuation performance while also generating measurable instantaneous voltage outputs under vibration. However, these voltage peaks—reaching a maximum of 1.64 V—represent only a laboratory-scale, proof-of-concept demonstration of electromechanical coupling rather than a practical or continuous form of energy harvesting, given the inherently sporadic nature of seismic excitation. Overall, the results confirm that the tested system is not a full metamaterial in the classical sense but rather a metamaterial-inspired periodic arrangement capable of inducing band-gap-based vibration attenuation. The inclusion of piezoelectric elements provides auxiliary sensing and micro-energy-generation capabilities, offering a preliminary foundation for future multifunctional seismic-protection concepts. Full article

Existing Mediterranean reinforced concrete buildings with masonry infills exhibit critical seismic vulnerabilities, yet real-time damage detection capabilities remain limited. This study validates a novel dense piezoelectric transducer (PZT) network concept for early damage detection in deficient RC structures under progressive seismic loading. A three-dimensional single-story RC frame with brick infills, representative of pre-Eurocode Mediterranean construction (non-ductile detailing, inadequate transverse reinforcement), was tested at serviceability limit states (SLSs) (Phase A) using a dynamic pushover approach with the 1978 Thessaloniki earthquake record, progressively scaled from EQ0.1g to EQ1.1g within the GREENERGY vertical forest renovation project. The specimen featured 48 PZTs using electromechanical impedance (EMI) methodology, 12 accelerometers, 8 displacement sensors, and 20 strain gauges. Progressive infill deterioration initiated at EQ0.5g while steel reinforcement remained elastic (max 2350 μstrain < 2890 μstrain yield). Maximum inter-story drift reached 11.37‰ with negligible residual drift (0.204‰). The PZT network, analyzed through Root Mean Square Deviation (RMSD), successfully detected internal cracking and infill-frame debonding before visible manifestation, validating its early warning capability. Floor acceleration amplification increased from 1.26 to 1.57, quantifying structural stiffness degradation. These SLS results provide critical baseline data enabling the Phase B implementation of sustainable vertical forest retrofitting strategies for aging Mediterranean building stock. Full article

Diesel engines remain the foundation for obtaining mechanical energy in sectors where autonomy and reliability are required; however, predictive diagnostics under real-world conditions remain challenging. The purpose of this scoping review is the investigation and systematization of published scientific data on the application of vibration methods for monitoring the technical condition of diesel engines in industrial or controlled laboratory conditions. Based on numerous results of publication analysis, sensor configurations, diagnosed components, signal analysis methods, and their application for assessing engine technical condition are considered. As methods for determining vibration parameters, time-domain and frequency-domain analysis, adaptive decompositions, and machine and deep learning algorithms predominate; high accuracy is more often achieved under controlled conditions, while confirmations of robustness on industrial installations are still insufficient. Key limitations for the application of vibration monitoring methods include the multicomponent and non-stationary nature of signals, a high level of noise, requirements for sensor placement, communication channel limitations, and the need for on-site processing; meanwhile, the assessment of torsional vibrations remains technically challenging. It is concluded that field validations of vibroacoustic data, the use of multimodal sensor platforms, noise-immune algorithms, and model adaptation to the specific environment are necessary, taking into account fuel quality, transient conditions, and climatic factors. Full article

Cutting force measurement plays a fundamental role in machining research and industrial applications, but existing dynamometers present important trade-offs between cost, stiffness, and dynamic bandwidth. Strain gauge devices are inexpensive but too flexible for high-speed operations, whereas piezoelectric systems provide excellent accuracy and bandwidth at prohibitive costs. This work presents the design, construction, and validation of a novel plate dynamometer based on polyvinylidene fluoride (PVDF) sensors, aimed at providing an effective alternative having an intermediate cost and suitable for advanced milling applications. The device integrates eight symmetrically arranged PVDF films in a stiff steel structure, complemented by four accelerometers for inertial compensation. A finite-element analysis confirmed favorable stress distribution at the PVDF contact surfaces and high resonance frequencies (under ideal clamping conditions). Modal tests demonstrated that uncompensated PVDF signals offer limited bandwidth, but the application of the Universal Inverse Filter (UIF) extended the usable bandwidth to 5 kHz along direct directions and up to 0.3–4 kHz along cross directions, approaching the performance of piezoelectric reference devices. Milling tests under diverse cutting conditions further validated the new device. Overall, the proposed device bridges the gap between low-cost strain gauge and high-performance piezoelectric dynamometers, offering a versatile and promising solution for both laboratory research and industrial applications. Full article

This paper focuses on the vibration analysis of a prototype helicopter rotor test stand, with particular attention to the dynamic response of its electric propulsion system. The stand is driven by an induction motor and equipped with composite rotor blades of various geometries, including blades with shape memory alloy (SMA)-based torsion actuators for angle of attack (AoA) adjustment. These variable geometries significantly influence the system’s dynamic behavior, where resonance phenomena may pose risks to structural integrity. The objective was to investigate how selected operational parameters specifically motor speed and AoA affect the vibration response of the propulsion system. Structural vibrations were measured using a tri-axial piezoelectric accelerometer system integrated with calibrated signal conditioning and high-resolution data acquisition modules. This setup enabled precise, time-synchronized recording of dynamic responses along all three axes. Fast Fourier Transform (FFT) and Power Spectral Density (PSD) methods were applied to identify dominant frequency components, including those associated with rotor harmonics and SMA activation. The highest vibration amplitudes were observed at an AoA of 16°, but all results remained within the vibration limits defined by MIL-STD-810H for rotorcraft drive systems. The study confirms the importance of sensor-based diagnostics in evaluating electromechanical propulsion systems operating under dynamic loading conditions. Full article

A modular, multi-functional (encompassing data acquisition, management, preprocessing, and transmission) sensing (MMFS) system based upon the Internet of Things (IoT) paradigm is discussed in this paper with the goal of continuous real-time, multi-sensor and multi-location monitoring of aircraft (including drones) structural performances during flight. According to industrial and system requirements, a microcontroller and four sensors (strain, acceleration, vibration, and temperature) were selected and integrated into the system. To enable the determination of potential in-flight failures and estimates of the remaining useful service life of the aircraft, resistance strain gauge networks, piezoelectric sensors for capturing structural vibrations and impact, accelerometers, and thermistors have been integrated into the MMFS system. Real flight tests with Evektor’s Cobra VUT100i and SportStar RTC aircraft have been undertaken to demonstrate the features of recorded data and provide requirements for the MMFS functional design. Real flight test data were analysed, indicating that a sampling rate of 1000 Hz is necessary to balance representation of relevant features within the data and potential loss of quality in fatigue life estimation. The design and evaluation of the performance of a prototype (evaluated via representative stress/strain experiments using an Instron Hydraulic 250 kN machine within laboratories) are detailed in this paper. Full article

Tactile sensing is increasingly vital in robotics, especially for tasks like object manipulation and texture classification. Among tactile technologies, optical and electrical sensors are widely used, yet no rigorous direct comparison of their performance has been conducted. This paper addresses that gap by presenting a comparative study between a high-resolution optical tactile sensor (a modified TacTip) and a low-resolution electrical sensor combining accelerometers and piezoelectric elements. We evaluate both sensor types on two tasks: texture classification and coefficient of dynamic friction prediction. Various configurations and resolutions were explored, along with multiple machine learning classifiers to determine optimal performance. The optical sensor achieved 99.9% accuracy on a challenging texture dataset, significantly outperforming the electrical sensor, which reached 82%. However, for dynamic friction prediction, both sensors performed comparably, with only a $\tilde{5}$% accuracy difference. We also found that the optical sensor retained high classification accuracy even when image resolution was reduced to 25% of its original size, suggesting that ultra-high resolution is not essential. In conclusion, the optical sensor is the better choice when high accuracy is required. However, for low-cost or computationally efficient systems, the electrical sensor provides a practical alternative with competitive performance in some tasks. Full article

Structural Health Monitoring (SHM) is an essential technique for continuously assessing structural conditions using integrated sensor systems during operation. SHM technologies have evolved to address the increasing demand for efficient maintenance strategies in advanced engineering fields, such as civil infrastructure, aerospace, and transportation. However, developing a miniaturized, cost-effective, and multi-sensor solution based on Wireless Sensor Networks (WSNs) remains a significant challenge, particularly for SHM applications in weight-sensitive aerospace structures. To address this, the present study introduces a novel WSN-based Multi-Sensor System (MSS) that integrates multiple sensing capabilities onto a 3 × 3 cm flexible Printed Circuit Board (PCB). The proposed system combines a Piezoelectric Transducer (PZT) for impact detection; a strain gauge for mechanical deformation monitoring; an accelerometer for capturing dynamic responses; and an environmental sensor measuring temperature, pressure, and humidity. This high level of functional integration, combined with real-time Data Acquisition (DAQ) and precise time synchronization via Bluetooth Low Energy (LE), distinguishes the proposed MSS from conventional SHM systems, which are typically constrained by bulky hardware, single sensing modalities, or dependence on wired communication. Experimental evaluations on composite panels and aluminum specimens demonstrate reliable high-fidelity recording of PZT signals, strain variations, and acceleration responses, matching the performance of commercial instruments. The proposed system offers a low-power, lightweight, and scalable platform, demonstrating strong potential for on-board SHM in aircraft applications. Full article