Search Results (105)

Wind turbines are increasingly deployed at larger scales and in harsher operating environments, leading to greater structural complexity, stronger load variability, and higher maintenance demands across both drivetrain and structural components. Reported field data indicate that gearboxes and bearings account for approximately 30–40% of total turbine downtime, while blade-related failures contribute roughly 20–25% of reported failure events, primarily through fatigue, delamination, leading-edge erosion, and lightning-induced defects. In parallel, large-scale and offshore turbines show increasing susceptibility to tower fatigue cracking, corrosion-assisted degradation, and flange joint bolt-preload loss under cyclic and environmental loading. This review provides a comprehensive applied-engineering synthesis of failure mechanisms, reliability challenges, and monitoring strategies for major wind turbine components, including gearboxes, bearings, blades, towers, and flange joints. A wide range of condition monitoring, structural health monitoring (SHM), and prognostics and health management (PHM) approaches is critically examined, including vibration analysis, acoustic emission, ultrasonic inspection, infrared thermography, impedance-based sensing, electromagnetic methods, machine vision, SCADA-based diagnostics, and artificial-intelligence-assisted fault classification. The review compares these techniques in terms of detectable damage types, spatial coverage, sensitivity, deployment practicality, and limitations under real operating conditions. In addition, statistical reliability methods and data-driven models are discussed to interpret failure trends and uncertainty. Recent AI-based studies have reported fault classification accuracies exceeding 90% under controlled or semi-controlled conditions; however, their field reliability remains constrained by data imbalance, domain shift, limited labeled failure datasets, model interpretability, and insufficient validation under realistic turbine operating environments. The main contribution of this review is an integrated applied synthesis that connects drivetrain and structural failure mechanisms with measurable monitoring indicators, diagnostic technologies, AI-enabled PHM limitations, and predictive-maintenance decision needs. The paper provides practical guidance for monitoring design, early fault detection, predictive maintenance, and long-term reliability improvement in next-generation wind turbine systems. Full article

Oil and gas pipelines are critical infrastructures that require continuous and reliable monitoring to detect leaks, pressure anomalies, corrosion, and unauthorized activities. Wireless sensor networks (WSNs) have emerged as an effective solution for large-scale pipeline monitoring due to their low deployment cost and real-time sensing capabilities. However, the resource-constrained nature of sensor nodes and the open wireless communication environment expose pipeline monitoring systems to various routing attacks, for example, blackhole, sinkhole, selective forwarding, and false data injection attacks, while simultaneously demanding strict energy efficiency to prolong network lifetime. In this paper, we propose SEER-PM (Secure and Energy-Efficient Routing for Pipeline Monitoring): a novel protocol that integrates an Artificial neural network (ANN)-based trust mechanism with energy-aware routing metrics. SEER-PM dynamically evaluates node trustworthiness based on packet forwarding behavior, residual energy, and signal consistency. By training the ANN on historical behavioral data, the system accurately detects malicious nodes with high precision. Simulation results demonstrate that SEER-PM outperforms existing secure routing protocols (Sec-AODV and T-LEACH) in terms of packet delivery ratio (PDR) by 14%, detection rate by 9.5%, and network lifetime by 12% under heavy attack scenarios. The proposed protocol enhances the reliability, security, and sustainability of pipeline monitoring WSNs operating in harsh and remote environments. Full article

Carbon/inorganic hybrid composites have evolved from filler-reinforced materials into design platforms for coupled electromagnetic, thermal, sensing, environmental, protective and energy-related functions. Their distinctive value lies in the possibility of combining a conductive, polarizable or porous carbon phase with an inorganic phase that contributes dielectric, magnetic, catalytic, ionic, thermally conductive or barrier behavior. This review examines carbon/inorganic hybrid multifunctional composites from the viewpoint of structure–property relationships, with emphasis on interfacial design, percolation, anisotropy, hierarchical architecture, processing and metrology. Selected graphitic composite studies are discussed as case studies for broadband dielectric spectroscopy, microwave shielding, high-frequency contact metrology, thermal diffusivity analysis and impedance-monitored graphene filters; these case studies are integrated with the broader international literature on CNT and graphene polymer composites, MXene films and foams, graphene/metal oxide photocatalysts, boron nitride/carbon thermal networks, biochar–graphene adsorbents, smart coatings, sensors, supercapacitors and water remediation systems. The central argument is that credible multifunctionality requires more than measuring several properties on the same material. It requires simultaneous or service-relevant co-optimization under constraints of thickness, density, processability, aging, humidity, corrosive media, regeneration, toxicity, economic feasibility and scalable fabrication. The review concludes with design rules and reporting recommendations intended to help move the field from impressive property demonstrations toward application-ready hybrid material systems. Full article

Developing an environmentally friendly material with dual functionality of corrosion monitoring and inhibition is crucial for reducing economic losses. Herein, dual-function phytic acid carbon dots (PA-CDs) were prepared with a hydrothermal treatment method for corrosion monitoring and inhibition. The as-prepared PA-CDs exhibited a distinct color change from brown-yellow to wine-red immediately in the presence of OH⁻, and H⁺ can restore the color of the PA-CDs-OH⁻ system from wine-red to brown-yellow. Inspired by the above phenomenon, a visible RGB-based colorimetric sensor was fabricated by combining PA-CDs coatings with the self-made RGB-sensing device to detect OH⁻/H⁺ during metal corrosion. In addition, PA-CDs had excellent corrosion inhibitory properties for Q235 steel hanging sheets in hydrochloric acid solutions, and the corrosion inhibition efficiency reached 96.40%. The excellent corrosion monitoring and inhibition properties demonstrate the potential application of the PA-CDs in engineering practice. Full article

Identifying corrosion-prone conditions early is a major maintenance challenge in closed, hard-to-access structural zones. This paper reports an in-service validation of the first monitoring layer of a multi-sensor data fusion approach for early warning of such conditions in selected closed zones of a medical rescue aircraft. The work covers sensor selection, installation in restricted-access compartments, and analysis of data from helicopter operations. Environmental, conductance, and electrochemical channels are combined to identify persistent conditions favorable to long-term corrosion development and to assign warning levels linked to maintenance actions. The thresholds proposed here are empirical screening criteria from the 82-day campaign, not universal damage thresholds or proof of existing corrosion. PZT and eddy-current sensing are planned as follow-up diagnostic layers in the overall architecture. These technologies have been validated separately under laboratory or controlled conditions but were not installed on the flying helicopter during this initial period. Although persistent severe early-warning episodes were detected, they did not coincide with an approved maintenance-access window suitable for additional PZT/EC hardware installation. The present results therefore characterize the corrosion-prone environment and the likelihood of corrosion initiation, not the type, exact location, pit depth, mass loss, or crack initiation of actual damage. Field inspection evidence of corrosion in hidden zones supports the practical relevance of early warning, while full end-to-end validation of localization and damage-growth monitoring remains future work. Full article

Conventional settlement monitoring techniques are inadequate for seawall construction environments due to severe physical impacts, the absence of terrestrial communication networks, and highly dynamic disturbances. This research proposes a multi-source fusion settlement monitoring system designed specifically for the construction phase to overcome these constraints. An integrated inclinometer–magnetoresistive sensing unit is the central component of this system. The unit achieves physical isolation from the severe impact loads of rock backfilling, guarantees protection in high-salinity and high-humidity environments, and accommodates the large deformations typical of soft foundations by utilizing a structural design that includes a rigid channel steel sheath, anti-corrosion sealing, and flexible joints. In terms of computation, a cascaded attitude fusion framework is developed that combines a Multiplicative Extended Kalman Filter (MEKF) with Quaternion Estimator (QUEST) initialization. High-precision displacement inversion via quaternion rotation is made possible by the introduction of an adaptive mechanism based on the Mahalanobis distance that precisely detects and suppresses transient acceleration disturbances induced by construction machinery and waves. Additionally, data transmission issues in remote offshore areas are resolved by combining solar power and BeiDou short-message communication technologies. This adaptive technique minimizes attitude estimate errors in dynamic situations by approximately 84.56%, as demonstrated by experimental and field validation. The system was deployed as a 165 m array comprising 49 sensing units and monitored continuously for 458 days, achieving a normalized RMSE of 9.44–11.02% compared to reference settlement tubes and capturing a maximum settlement of 1.7 m in the core high-fill section. These results confirm the system’s high monitoring accuracy and resilience in harsh construction conditions. Full article

Wind turbine blades serve as the core components of wind energy conversion systems, and their safe and stable operation is pivotal to the operational efficiency and reliability of wind farms. However, prolonged operation in harsh environmental conditions such as strong winds, heavy rainfall, ultraviolet radiation, and temperature fluctuations renders wind turbine blades susceptible to fatigue damage and structural failure. Aiming at the drawbacks of traditional electromagnetic sensors, including their vulnerability to lightning strikes and poor corrosion resistance, as well as the elastic modulus mismatch between existing fiber Bragg grating (FBG)-encapsulated sensors and wind turbine blade structures, this study selects the ethylene–propylene–diene monomer (EPDM) as the encapsulation material to develop EPDM-FBG strain sensors. The effectiveness of the proposed sensor in blade strain monitoring is ultimately verified via static load model tests on small-scale wind turbine blades. Test results demonstrate that the EPDM-FBG strain sensor exhibits excellent static strain sensing performance, with its test results being highly consistent with those of bare FBG sensors and a relative error of less than 5%, which can fully meet the practical requirements of static strain monitoring for wind turbine blades. This research provides a novel and reliable monitoring method for the health monitoring of wind turbine blades. Full article

Urban pipelines are essential infrastructure components in modern cities. Their curved and confined structures make sensing difficult to achieve. In conventional flexible sensing devices, pressure and bending signals often interfere with each other. To address this problem, we propose an integration strategy for multi-array sensors on flexible printed circuits. The approach integrates laser-induced graphene pressure sensors and bending sensors on a polydimethylsiloxane substrate with flexible printed circuits. This integration enables stable and reliable signal acquisition and the device shows good performance under pressure loading. It has high linearity (R² > 0.99), low hysteresis (2.68%), and a fast response time (~50 ms) in the range of 0–120 kPa. The sensing architecture is based on geometry-induced strain-field differentiation, which suppresses pressure–bending cross-interference and improves multimodal signal discrimination through structural design. Pressure mainly produces isotropic signals, while bending generates anisotropic strain signals. We test the device in simulated pipeline environments. Protrusion defects and corrosion defects generate different signal patterns. These differences allow clear defect identification. The device further supports spatial posture sensing and bending-state monitoring in complex curved pipeline conditions. Full article

Carbon dots (CDs) have demonstrated promising application prospects in the field of corrosion protection due to their small size, excellent dispersibility, abundant and tunable surface functional groups, low cost, environmental friendliness, and unique fluorescence properties. However, existing reviews have predominantly focused on the synthesis and photoluminescence properties of CDs, lacking systematic integration and in-depth mechanistic analysis of their diverse applications in corrosion protection. This review systematically summarizes the recent research progress and underlying mechanisms of CDs in five key areas: corrosion inhibitors, anticorrosive coatings, photogenerated cathodic protection, chloride binding, and corrosion monitoring. As corrosion inhibitors, CDs form compact protective films on metal surfaces through synergistic physical and chemical adsorption. In anticorrosive coatings, CDs not only enhance the physical barrier effect but also impart intelligent functionalities such as self-healing and corrosion monitoring. In the field of photogenerated cathodic protection, CDs broaden the light absorption range of semiconductors and facilitate the separation of photogenerated carriers. As chloride binding promoters, CDs promote the formation of cement hydration products, thereby improving the durability of reinforced concrete structures. As sensing platforms, CDs enable early visual detection of corrosion through their specific fluorescence response to ions such as Fe³⁺. Despite significant progress, challenges remain in scalable preparation, practical application performance in complex environments, and multifunctional integration. This review systematically outlines the research advancements of CDs in corrosion protection, providing a practical reference for subsequent studies and engineering applications. Future research should focus on scalable synthesis, machine learning-assisted design, and the development of integrated multifunctional protection systems to promote the practical application of CDs in the field of corrosion protection. Full article

Pipelines play a vital role in transporting oil, gas, water, and other critical resources across vast distances. However, they are often exposed to harsh environmental conditions, aging, corrosion, and mechanical stresses that can lead to structural degradation or failure. Structural health monitoring (SHM) offers a proactive solution for ensuring the integrity and safety of pipeline systems through continuous or periodic assessment using advanced sensing technologies and analytical methods. This paper presents the use of Lamb waves to find surface cracks in pipelines. Finite element software, ABAQUS/CAE 2017, is used to simulate intact and damaged pipes. The Time of Flight (ToF) method is applied with two techniques. The first is based on the difference between the received waves for damaged and intact pipelines, while the second is based on the difference between two sensor reads in damaged pipelines. The effectiveness of SHM systems in detecting anomalies and guiding maintenance decisions is evaluated. The results demonstrate the potential of SHM to enhance pipeline reliability, reduce downtime, and support condition-based maintenance strategies. This research contributes to the development of smarter, safer, and more efficient pipeline monitoring systems. Full article

To evaluate the behavior of industrial equipment from a corrosion point of view, it is mandatory to consider both the material that equipment is made from and the working conditions such as temperature, pH, and the existing microorganisms in the working environment. Our studies regarding ecotoxicological monitoring of biological suspensions Diatomee, Saccharomyces, and Spirulina (DSS) are focused on three directions: (1) the evolution of chemical and biological parameters of the reaction environment (pH, conductivity, TDS, DO, OD), the kinetics of DSS microorganisms’ growing curve; (2) the analysis of biofilm forming on the exposed metallic surface and (3) the analysis of corrosion degree (phenomena) of tested metals in five media, by using the corrosion indices: volumetric index, gravimetric index, and penetration index. The viability of microorganisms in the presence of aluminum, zinc, and iron shows the following sequence: Al_Diat > Fe_Diat > Zn_Diat > Al_Spir > Zn_Spir > Al_Sach > Zn_Sach > Fe_Spir > Fe_Sach. The development of biofilms on the surface of metal plates followed the sequence outlined below: Al_Diat > Fe_Diat > Zn_Diat > Fe_Spir > Zn_Sach > Fe_Sach > Al_Sach > Zn_Spir > Al_Spir. Iron exhibits the most favorable performance, displaying a very low Ip value across all tested environments, including salt water. Aluminum demonstrates sensitivity to specific biological environments, with the highest degree of corrosion observed in Spirulina, indicating that not all biological environments confer protection to aluminum. Diatoms and Saccharomyces suspensions exert an inhibitory effect on corrosion. Zinc is the most susceptible metal, experiencing the greatest corrosion in Spirulina, followed by salt water, while biological environments only partially mitigate the corrosion rate. Full article

To address force uniformity and corrosion issues in slope reinforcement, this paper presents a field implementation of pressure-uniformly-dispersed Carbon-Fiber-Reinforced Polymer (CFRP) anchor cables integrated with Fiber Bragg Grating (FBG) sensing technology. A tensioning trial was conducted on a dangerous rock project on Guangyang Island, Chongqing, utilizing a distributed FBG array (0.5 m spacing) for full-length strain monitoring. The results confirm that, under the specific conditions of this project, the anchorage segment exhibits the characteristic two-stage behavior of “delayed activation–uniform bearing” previously documented in bonded anchor systems, with a critical transition observed at approximately 120 kN. Beyond this threshold, the anchorage efficiency reached approximately 85%, validating the three-stage uniformly dispersed design for this specific geological context. While the load transfer mechanism aligns with established bonded anchor mechanics, this study demonstrates the practical feasibility of high-resolution distributed sensing in CFRP anchor systems, providing benchmark data for construction quality control and long-term health monitoring of similar slope reinforcement projects. Full article

Steel structures in marine splash zones (MSZ) experience severe corrosion owing to high humidity and frequent wet–dry cycles, which poses considerable threats to structural integrity and operational safety. To achieve intelligent, real-time corrosion monitoring, this study presents a corrosion-rate model based on the Weibull distribution, intended to serve as the core algorithm of smart corrosion sensors that continuously provide corrosion depth data via techniques such as electrochemical impedance spectroscopy or fiber optic sensing. The model was validated through systematic laboratory salt-spray cyclic tests that simulated MSZ conditions; corrosion behaviour was analysed by means of mass-loss measurements, electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). The results reveal a three-stage corrosion progression and confirm that the Weibull model accurately captures the time-variant corrosion behaviour under different splash intensities. The model thus provides a reliable algorithmic foundation for intelligent corrosion monitoring, enabling real-time assessment of structural safety and prediction of residual life. Full article

The presence of corrosion on metal substrates can compromise the integrity of a structure. In this study, discrete fiber Bragg grating (FBG) sensing technology is used to monitor the corrosion process of a Cu surface in a corrosive environment. The surface morphology and nanostructures of corrosion products are observed to reveal their forms or structures. The results show that corrosion products are composed of loose or dense collections of micrometer/nanometer-sized particles in a crystalline state. Furthermore, this study experimentally explores how localized corrosion affects the sensing signal between an optical fiber and a corroded copper substrate. This phenomenon is also theoretically modeled using contact mechanics, enabling a semi-quantitative calculation of how corrosive particles influence Bragg wavelength. The wavelength shift trends of the four monitoring points over a period of 10 days were similar under the same corrosive environment. Results show that fiber Bragg grating sensors can identify the corrosion process in real time using wavelength demodulation methods. This study will lay the foundation for subsequent work on the precise monitoring of copper corrosion using fiber Bragg grating sensors. Full article

High-strength bolted connections are fundamental load-bearing components in critical engineering infrastructures such as wind turbines, bridges, and heavy machinery. Under complex service environments involving dynamic loading, vibration, corrosion, and temperature variations, bolts are prone to interacting failure mechanisms, including fatigue fracture, corrosion-assisted cracking, hydrogen embrittlement, and progressive preload loss, which pose significant challenges for reliable condition monitoring and early fault diagnosis. This review provides a structured synthesis of recent advances in bolt health monitoring and intelligent fault diagnosis. A unified framework is established to link multi-physics failure mechanisms with multi-modal sensing technologies and data-driven diagnostic methods. Key sensing approaches—such as piezoelectric impedance techniques, ultrasonic phased array inspection, and computer vision-based monitoring—are critically reviewed in terms of their physical principles, diagnostic capabilities, and limitations. Furthermore, the transition from traditional model-based and signal-processing-driven methods to machine learning- and deep learning-based approaches is examined, with emphasis on multi-modal data fusion, real-time monitoring, and lifecycle-oriented health management enabled by IoT and digital twin technologies. Finally, key challenges and future research directions toward robust and scalable intelligent bolt health management systems are outlined. This review’s primary contribution lies in establishing a novel, integrated framework that links failure physics to sensing and diagnosis, thereby providing a structured roadmap for transitioning from isolated component monitoring to lifecycle-oriented, intelligent health management systems for critical bolted connections. Full article