Search Results (126)

The magnetic flux leakage (MFL) method is widely acknowledged as a highly effective non-destructive evaluation (NDE) technique for detecting local damage in ferromagnetic structures such as steel wire ropes. In this study, a multi-channel MFL sensor module was developed, incorporating a purpose-designed Hall sensor array and magnetic yokes specifically shaped for steel cables. To validate the proposed damage detection method, artificial damages of varying degrees were inflicted on wire rope specimens through experimental testing. The MFL sensor module facilitated the scanning of the damaged specimens and measurement of the corresponding MFL signals. In order to improve the signal-to-noise ratio, a comprehensive set of signal processing steps, including channel equalization and normalization, was implemented. Subsequently, the detected MFL distribution surrounding wire rope defects was transformed into MFL images. These images were then analyzed and processed utilizing an object detection method, specifically employing the YOLOv9 network, which enables accurate identification and localization of defects. Furthermore, a quantitative defect detection method based on image size was introduced, which is effective for quantifying defects using the dimensions of the anchor frame. The experimental results demonstrated the effectiveness of the proposed approach in detecting and quantifying defects in steel cables, which combines deep learning-based analysis of MFL images with the non-destructive inspection of steel cables. Full article

Steel wire ropes used in transport and aerospace applications are critical components whose failure can lead to significant safety, operational, and environmental consequences. Current diagnostic practices based on magnetic rope testing (MRT) often suffer from signal misalignment and subjective interpretation, particularly under varying operational conditions or in polymer-impregnated ropes with delayed damage indicators. This study explores the application of the Dynamic Time Warping (DTW) algorithm to enhance the reliability of MRT diagnostics. The research involved analyzing long-term MRT recordings of wire ropes used in mining operations, including different scanning resolutions and signal acquisition methods. A mathematical formulation of DTW is provided along with its implementation code in R and Python. The DTW algorithm was applied to synchronize diagnostic signals with their baseline recordings, as recommended by ISO 4309:2017 and EN 12927:2019 standards. Results show that DTW enables robust alignment of time series with slowly varying spectra, thereby improving the comparability and interpretation of MRT data. This approach reduces the risk of unnecessary rope discard and increases the effectiveness of degradation monitoring. The findings suggest that integrating DTW into existing diagnostic protocols can contribute to safer operation, lower maintenance costs, and reduced environmental impact. Full article

The objective of this paper is to evaluate the capability of various steel rope manufacturers to maintain the desired variability within the strength class of wires used in the production of steel ropes. From a service life perspective, it is optimal to achieve the narrowest possible strength class interval for wires integrated into steel ropes. However, the applicable EN 12385 standards permit a relatively wide interval of allowable strength class dispersion. The analysis encompasses 112 steel ropes tested over the period from 2000 to 2025. For the purpose of evaluating rope quality in terms of wire strength variability, the ropes were categorized into four quality classes. The assessment of wire strength was conducted using statistical quality control methods, specifically through the application of control charts. Based on these methods, the stability and capability of wire strength within each rope were verified. The results highlight the differences in wire strength performance across the evaluated quality classes. Full article

As an indispensable piece of production equipment in the industrial field, wire rope is directly related to personnel safety and the normal operation of equipment. Therefore, it is necessary to perform broken wire detection. Deep learning has powerful feature-learning capabilities and is characterized by high accuracy and efficiency, and the YOLOv8 object detection model has been adopted to detect wire breaks in electromagnetic signal images of wire rope, achieving better results. Nevertheless, the black box problem of the model brings a new trust challenge, and it is difficult to determine the correctness of the model’s decision and whether it has any potential problems, so an interpretability study needed to be carried out. In this work, a perturbation-based interpretability method—ESTC (Eliminating Splicing and Truncation Compensation)—is proposed, which distinguishes itself from other methods of the same type by targeting the signaling object instead of the ordinary object. ESTC is compared with other model-agnostic interpretable methods, LIME, RISE, and D-RISE, using the same model on the same test set. The results indicate that our proposed method is objectively superior to the others, and the interpretability analysis shows that the model predicts in a way that is consistent with the priori knowledge of the manual rope inspection. This not only increases the credibility of using the object detection model for broken wire detection but also has important implications for the practical application of using object detection model to detect wire breaks. Full article

This paper focused on the motion control of an underwater vehicle installed on a linear guide system, which is driven by two electric winches with wire ropes. The vehicle is subject to complex nonlinear time-varying disturbances and actuator input saturation effects during motion. A coupled dynamic model, incorporating an underwater vehicle, winches, and wire ropes, was established. Particular attention was paid to the nonlinear time-varying hydrodynamic disturbances acting on the underwater vehicle. The Kelvin–Voigt model was introduced to characterize the nonlinear dynamic behavior of the wire ropes, enabling the model to capture the dynamic response characteristics of traction forces. To tackle cross-coupling within the towing system, a differential tension coordination control method was proposed that simultaneously regulates system tension during motion control. For the vehicle dynamics model, a nonsingular fast-terminal sliding-mode (NFTSM) controller was designed to achieve high-precision position tracking control. An auxiliary dynamic compensator was incorporated to mitigate the impact of actuator input saturation. To handle time-varying disturbances, a fuzzy adaptive nonlinear disturbance observer (FANDO) is developed to perform feedforward compensation. Stability proof of the proposed algorithms was provided. Extensive numerical simulations demonstrate the effectiveness of the control strategies. Compared to the NFTSM without the disturbance observer the absolute mean value of the tracking error decreased by 76%, the absolute maximum value of the tracking error decreased by 67%, and the mean square error decreased by 93.5%. Full article

This study experimentally and numerically investigated the effectiveness of fiber-core steel wire ropes (FC-SWRs) in enhancing the flexural performance of reinforced concrete (RC) T-beams using a bonding technique. The investigation focused on deflection, flexural load-carrying capacity, and failure modes, along with key behaviors such as ductility, stiffness, energy absorption, and steel strain response. Two beams were tested under four-point bending until failure—one serving as the control specimen and the other strengthened with bonded FC-SWRs to improve its flexural behavior. Additionally, an analytical study was conducted using a computer program based on the Modified Compression Field Theory (MCFT), and the results were compared with experimental findings. The validation of the analytical model enabled further parametric investigations, examining the influence of the FC-SWR diameter, modulus of elasticity, and steel reinforcement ratio on flexural performance. Full article

A novel mechanism for seamless morphing trailing edge flaps is presented in this paper. This bio-inspired morphing concept is derived from an elephant’s trunk and is called the Elephant Trunk Mechanism (ETM). The structural flexibility of an elephant’s trunk and its ability to perform various types of deformations make it a promising choice in morphing technology for increasing the performance of continuous and smooth downward bending deformation at a trailing edge. This mechanism consists of a number of tooth-like elements attached to a solid wing box; the contractions of these tooth-like elements by external actuation forces change the trailing edge shape in the downwards direction. The main actuation forces are applied through wire ropes passing through tooth-like elements to generate the desired contractions on the flexible teeth. A static structural analysis using the Finite Element Method (FEM) is performed to examine this novel morphing concept and ensure its structural feasibility and stability. Topology optimization is also performed to find the optimum configuration with the objective of reducing the structural weight. The optimized mechanism is then attached to the flap section of a UAS-S45 wing. Finally, a skin analysis is performed to find its optimum skin material, which corresponds to the requirements of the morphing flap. The results of structural analysis and topology optimization reveal the reliability and stability of the proposed mechanism for application in the Seamless Morphing Trailing Edge (SMTE) flap. The optimization results led to significant improvements in the structural parameters, in addition to the desired weight reduction. The ETM maximum vertical displacement increased by 8.6%, while the von Mises stress decreased by 10.43%. Furthermore, the factor of safety improved from 1.3 to 1.5, thus indicating a safer design. The mass of the structure was reduced by 35.5%, achieving the primary goal of topology optimization. Full article

Proper surface lubrication with optimal grease thickness is essential for extending steel wire rope service life. To achieve automated lubrication quality control and address challenges like variable lighting and motion blur that degrade recognition accuracy in practical settings, this paper proposes an improved lightweight GEMR-MobileViT. The model is designed to identify the grease thickness on steel wire rope surfaces while mitigating the high parameters and computational complexity of existing models. In this model, part of the standard convolution is replaced by GhostConv, a novel efficient multi-scale attention (EMA) module is introduced into the local expression part of the MobileViT block, and the structure of residual connections within the MobileViT block is designed. A transfer learning method is then employed. A custom dataset of steel wire rope lubrication images was constructed for model training. The experimental results demonstrated that GEMR-MobileViT achieved a recognition accuracy of 96.63% across five grease thickness categories, with 4.19 M params and 1.31 GFLOPs computational complexity. Compared to the pre-improvement version, recognition accuracy improved by 4.4%, while its parameters and computational complexity were reduced by 15.2% and 10.3%, respectively. When compared with current mainstream classification models such as ConvNeXtV2, EfficientNetV2, EdgeNeXt, NextViT, and MobileNetV4, our GEMR-MobileViT achieved superior recognition accuracy and demonstrated significant advantages in its model parameters, striking a good balance between recognition precision and model size. The proposed model facilitates deployment in steel wire rope lubrication working sites, enabling the real-time monitoring of surface grease thickness, thereby offering a novel approach for automating steel wire rope maintenance. Full article

This study presents an on-line intelligent lubrication system utilizing specialty grease to address lubricant loss and uneven coating issues in traditional methods. Characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FT-IR), the specialty grease demonstrates superior tribological performance, achieving a 46.7% reduction in the average friction coefficient and 33.3% smaller wear scar diameter under a 392 N load compared to conventional lubricants. The system features an automatic control vehicle design integrating heating, grease supply, lubrication-scraping mechanisms, and a dual closed-loop intelligent control system combining PID-based temperature regulation with machine vision. Experiments identified 50 °C as the optimal heating temperature. Kinematic modeling and grease consumption analysis guided greasing parameters optimization, validated through simulations and practical tests. Evaluated on a 20 m long, 36.5 mm diameter wire rope, the system achieved full coverage within 60 s, forming a uniform lubricant layer of 0.3–1.0 mm thickness (±0.15 mm deviation). It realizes the innovative application of high-adhesion lubricating grease, adaptive process control, and real-time thickness feedback technology, significantly improving the lubrication effect, reducing maintenance costs, and extending the lifespan of the wire rope. This provides intelligent lubrication technology support for the reliable operation of wire ropes in industrial fields. Full article

Offshore cranes are widely used in important fields such as wind power construction and ship replenishment. However, large payloads such as wind turbine blades are hoisted by multiple steel wire ropes, which makes it difficult to directly control their movements; that is, the number of input degrees of freedom is less than that of the output degrees of freedom. In addition, compared with land cranes, offshore cranes are inevitably affected by waves, wind, etc. The transition from a fixed base to a dynamic base brings severe challenges to their oscillation suppression and precise positioning. At the same time, to improve operational efficiency, the hoisting operation of offshore cranes usually adopts velocity input control patterns that fit the habits of manual operation, and most of them are in the form of dual-axis linkage for pitch and hoisting. Therefore, this paper proposes a fast terminal sliding mode control method for double-pendulum offshore cranes with distributed-mass payloads (DMPs). First, a nonlinear dynamic model of offshore cranes considering DMPs is established, and a dynamic model based on acceleration input control patterns is acquired. Based on this, considering the variation in hoisting rope lengths, a novel adaptive control method is proposed. Finally, simulation results verify the effectiveness of the proposed method, and the robustness of the proposed method to DMP mass parameter uncertainty and disturbances is demonstrated. Full article

Wire rope greasing is essential for protecting wire ropes from corrosion and wear. To address issues such as low maintenance efficiency and excessive grease usage, this study proposes a high-efficiency, low-consumption optimization control method for the wire rope greasing process. A time objective function for the greasing process and a consumption objective function for grease are established. Considering the actual constraints of greasing equipment performance and greasing quality, a multi-objective optimization model is developed with greasing speed, greasing thickness, grease flow rate, and greasing time as the optimization parameters. The model aims to achieve high efficiency (minimizing greasing process time) and low consumption (minimizing grease consumption). Weight coefficients are introduced to transform the multi-objective optimization model into a single-objective optimization model, which is then solved using an improved genetic algorithm. The effectiveness of the model is validated through a specific case study, and a sensitivity analysis of the weight coefficients of the objective functions in the optimization model is conducted. This research provides valuable support for wire rope greasing process planning and improvement. Full article

Significant vibrations of the traction wire rope can impact the efficiency and accuracy of static testing in wind turbine blade assessments. This study focuses on the vibration characteristics of the wire rope under static loading conditions. A simulation model for single-point static tests of wind turbine blades was developed using Adams software and validated through wire rope tension and longitudinal acceleration measurements during static tests on a full-scale 71.5-m blade. The validated model was used to analyze the effects of wire rope span and pulley position on vibration amplitude and tension in single-point loading scenarios. The results show that increasing the wire rope span and the distance between the pulley and blade fixture significantly amplifies vibration. Adjusting the span of the wire rope and the pulley position causes the primary vibration frequency to approach the natural frequency, leading to a substantial increase in vibration near the resonance frequency. To avoid resonance and reduce vibration, it is recommended to use two misaligned ground tracks, ensuring the wire rope span does not exceed 30 m and the distance between the pulley and blade fixture does not exceed 7 m. Specific resonance combinations of wire rope span and pulley position should be avoided to improve the precision and reliability of the testing system. Full article

Steel wire rope serves as a critical load-bearing and transmission component in the transportation equipment utilized in coal mines. It exhibits various forms of damage during prolonged service, which significantly jeopardizes the safety reliability of the transportation equipment. To investigate the formation process of the surface wear of steel wire rope and its influence on mechanical properties, the tribological evolution and performance degradation of the transmission wire rope under rope–sheave contact and rope–rope contact were studied in this paper. The wire rope’s friction coefficient (COF) is stable between 0.7 and 0.8 under two contact conditions. It is more likely to lead to the accumulation of frictional heat under the condition of rope–sheave contact. The primary wear mechanisms of transmission wire ropes are adhesive wear and fatigue wear. Additionally, the wear evolution leads to the nonlinear degradation of the tensile strength and bending fatigue life of the wire rope, and the wear damage caused by rope–sheave contact is more harmful. As the sliding distance between the wire rope and the sheave increases, the breaking force decreases from approximately 48 kN to 23 kN, and the number of bending fatigues of the wire rope before scrapping is reduced from approximately 6200 times to 200 times. Full article

This work proposes an adaptive rapid terminal SMC (sliding mode control) approach based on the FFTSO (fast finite-time state observer) for overhead crane trajectory tracking and anti-swing control in the presence of external disturbances and parameter uncertainty. First, the system state observation under the constraint of unknown system parameters is accomplished by designing the FFTSO based on finite-time theory. Next, a parameter-adaptive fast terminal SMC is created for an overhead crane based on the model transformation. This technique can still monitor the intended trajectory and reduce payload swing even in cases when the payload mass and wire rope length are uncertain. Next, the Lyapunov theorem is used to demonstrate the stability of the overhead crane system’s positioning and anti-swing angle control mechanism. Lastly, the platform experiments confirm that the suggested closed-loop system control technique is successful. Full article

Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian framework for MFL signal analysis in SWR inspection. Our approach integrates discrete wavelet transform with adaptive thresholding and multi-scale feature fusion, enabling simultaneous detection of minute defects and large-area corrosion. To validate our method, we implemented a four-channel MFL detection system and conducted extensive experiments on both simulated and real-world datasets. Compared with state-of-the-art methods, including long short-term memory (LSTM), attention mechanisms, and isolation forests, our approach demonstrated significant improvements in precision, recall, and F1 score across various tolerance levels. The proposed method showed superior detection performance, with an average precision of 91%, recall of 89%, and an F1 score of 0.90 in high-noise conditions, surpassing existing techniques. Notably, our method showed superior performance in high-noise environments, reducing false positive rates while maintaining high detection sensitivity. While computational complexity in real-time processing remains a challenge, this study provides a robust solution for non-destructive testing of SWR, potentially improving inspection efficiency and defect localization accuracy. Future work will focus on optimizing algorithmic efficiency and exploring transfer learning techniques for enhanced adaptability across different non-destructive testing (NDT) domains. This research not only advances signal processing and anomaly detection technology but also contributes to enhancing safety and maintenance efficiency in critical infrastructure. Full article