Complex Working Conditions Research Articles

Synergistic anti-corrosion and anti-wear of epoxy coating functionalized with inhibitor-loaded graphene oxide nanoribbons

The synergy between corrosion protection and wear resistance is an effective strategy for the development of multifunctional coating to withstand complex working conditions. This study reports an epoxy resin coating filled with benzotriazole loaded metal-organic frameworks (BTA-MOFs) functionalized graphene oxide nanoribbons (GONR) that exhibit active anti-corrosion, act as a barrier to corrosive ion, and enhance wear resistance. The GONR@BTA-MOFs composite is synthesized through chemically etching multi-walled carbon nanotubes and subsequent electrostatic self-assembly corrosion inhibitors loaded MOFs onto the GONR. The composite demonstrates improved compatibility with epoxy resins compared to carbon nanotubes. The anti-corrosion performance of the composite coating is investigated using electrochemical impedance spectroscopy. After immersing in a 3.5 wt.% NaCl solution for 25 d, the alternating current impedance of the composite coating is three orders of magnitude higher than that of pure epoxy resin. Simultaneously, the controlled release of the corrosion inhibitor retards the deterioration of the coating after localized damage occurrence, which functions as active corrosion protection. The GONR@BTA-MOFs/EP composite coating exhibits the highest corrosion potential of -0.188 V and the lowest corrosion current of 3.162 × 10−9 A cm−2) in the Tafel test. Tribological studies reveal a reduction in the friction coefficient from 0.62 to 0.08 after incorporating GONR@BTA-MOFs in the coating, with the wear volume being seven times lower than that of pure epoxy resin. The excellent lubrication effect of the nanomaterials reduces the coefficient of friction of the coating, thereby improving the abrasion resistance of the coating. The synergy between the self-lubrication of the two-dimensional layered fillers and the corrosion resistance of the smart inhibitor containers suggests a promising strategy for enhancing the performance of epoxy resins under complex working conditions.

Composite fault diagnosis of gearbox based on deep graph residual convolutional network

In gearbox systems, a composite fault diagnosis resulting from mutual interference among different components poses a significant challenge. The traditional composite fault diagnosis methods based on conventional signal analyses and feature extractions often suffer from low sensitivity to fault characteristics and difficulty in effectively identifying composite faults. On the other hand, composite fault diagnosis research via deep learning and data-driven approaches typically faces issues such as incomplete training datasets and insufficient exploration of feature correlation information, leading to an underutilization of the fault information. Therefore, this paper proposes a deep graph residual convolutional neural network (DGRCN) based on feature correlation mining for composite fault diagnosis in gearboxes. First, Pearson correlation coefficients are utilized to explore the relationships among features in the traditional feature set, transforming these relationships into a graph-structured feature set. Next, a deep graph residual convolutional network is constructed by integrating deep graph structures into a residual framework. This network globally extracts composite fault subgraph features and explores local feature correlations. Finally, the model is trained via various composite fault datasets under complex working conditions, achieving the diagnosis and identification of composite faults under the constraint of limited samples. The experimental results demonstrate that the proposed method significantly improves composite fault diagnosis accuracy, outperforming commonly used methods in this field.

Evaluation of the Competency and Skills Needs for Seaplane Maintenance in Pilot Education at the Indonesian Pilot Academy Banyuwangi

This research assesses the competency and skill requirements for Seaplane aircraft maintenance in the pilot education program at the Indonesian Pilot Academy Banyuwangi. Utilizing qualitative methods, including in-depth interviews and direct observation, the study involves students and teachers in the pilot education program. Analysis reveals crucial competencies for students, encompassing a profound understanding of Seaplane aircraft systems, adept problem-solving during operations, and the capacity for quick and precise decision-making. Special skills, such as adaptability to complex working conditions and meticulousness in maintenance procedures, are also deemed essential. The research identifies alignment between the academy's curriculum and Seaplane aircraft maintenance needs, yet highlights specific areas requiring enhancement. The study recommends strengthening the curriculum and teaching methods to better address competency and skill needs. Implementing these recommendations is anticipated to elevate the quality of graduates, ensuring they are well-equipped to meet the challenges of Seaplane aircraft maintenance in the aviation industry.

Improving Pilot Competence Through Flying Practice Learning Using Flight Simulator at the Indonesia Civil Pilot Academy of Banyuwangi

This research assesses the competency and skill requirements for Seaplane aircraft maintenance in the pilot education program at the Indonesian Pilot Academy Banyuwangi. Utilizing qualitative methods, including in-depth interviews and direct observation, the study involves students and teachers in the pilot education program. Analysis reveals crucial competencies for students, encompassing a profound understanding of Seaplane aircraft systems, adept problem-solving during operations, and the capacity for quick and precise decision-making. Special skills, such as adaptability to complex working conditions and meticulousness in maintenance procedures, are also deemed essential. The research identifies alignment between the academy's curriculum and Seaplane aircraft maintenance needs, yet highlights specific areas requiring enhancement. The study recommends strengthening the curriculum and teaching methods to better address competency and skill needs. Implementing these recommendations is anticipated to elevate the quality of graduates, ensuring they are well-equipped to meet the challenges of Seaplane aircraft maintenance in the aviation industry.

Improved MobileNet V3-Based Identification Method for Road Adhesion Coefficient.

To enable the timely adjustment of the control strategy of automobile active safety systems, enhance their capacity to adapt to complex working conditions, and improve driving safety, this paper introduces a new method for predicting road surface state information and recognizing road adhesion coefficients using an enhanced version of the MobileNet V3 model. On one hand, the Squeeze-and-Excitation (SE) is replaced by the Convolutional Block Attention Module (CBAM). It can enhance the extraction of features effectively by considering both spatial and channel dimensions. On the other hand, the cross-entropy loss function is replaced by the Bias Loss function. It can reduce the random prediction problem occurring in the optimization process to improve identification accuracy. Finally, the proposed method is evaluated in an experiment with a four-wheel-drive ROS robot platform. Results indicate that a classification precision of 95.53% is achieved, which is higher than existing road adhesion coefficient identification methods.

Energy Management Strategy of Fuel Cell Commercial Vehicles Based on Adaptive Rules

Fuel cell vehicles have been widely used in the commercial vehicle field due to their advantages of high efficiency, non-pollution and long range. In order to further improve the fuel economy of fuel cell commercial vehicles under complex working conditions, this paper proposes an adaptive rule-based energy management strategy for fuel cell commercial vehicles. First, the nine typical working conditions of commercial vehicles are classified into three categories of low speed, medium speed and high speed by principal component analysis and the K-means algorithm. Then, the crawfish optimization algorithm is used to optimize the back propagation neural network recognizer to improve the recognition accuracy and optimize the rule-based energy management strategy under the three working conditions to obtain the optimal threshold. Finally, under WTVC and combined conditions, the optimized recognizer is used to identify the conditions in real time and call the optimal rule threshold, and the sliding average filter is used to filter the fuel cell output power in real time, which finally realizes the adaptive control. The simulation results show that compared with the conventional rule-based energy management strategy, the number of fuel cell start–stops is reduced. The equivalent hydrogen consumption is reduced by 7.04% and 4.76%, respectively.

Configuration design methodof hybrid electric agricultural machinery system based on layered two-dimensional matrix

The working environment and objects of agricultural machinery are different from those of automobiles, andagricultural machinery is greatly affected by the working environment and working conditions, and the power output systemismore complex. Agricultural machinery not only has drive output, but also PTO output and hydraulic output, whichtogetherconstitute the output system of agricultural machinery. Agricultural machinery conditions can be dividedintoroadtransportation working conditions and field operation working conditions. The working conditions of agricultural machinerycan be divided into different load conditions according to the different traction tools and whether the hydraulic andPTOwork,such as ploughing, rotary tillage, fertilization, and transportation. Therefore, developing hybrid electric agricultural machinerysystems that are suitable for various complex working conditions holds great theoretical significance and practical value. Giventhe complex working conditions of agricultural machinery systems in agricultural work and the intricate challengesindesigning hybrid agricultural machinery systems. In this paper, the two-dimensional matrix is used to represent thephysicalstructure and dynamics of the multi-channel power output agricultural mechanism. A hierarchical two-dimensional matrixmethod for the generation and screening of hybrid electric agricultural machinery systems with multi-power output powerisproposed. The components of agricultural machinery are divided into an input layer and an output layer, and these componentsare coded and defined, and then transformed into a matrix. The hierarchical two-dimensional matrix method is usedtogenerateand screen the hybrid electric agricultural mechanism type. Through the stratification of the matrix, the complexityoftheconfiguration generation is reduced, and the constraints are applied to the basic screening of the generated configurations. Therationality of the configurations obtained after generation and screening is verified by Simulink simulation. The resultsshowthat the configuration screened by this method can meet the performance requirements of agricultural machinery.

Trajectory-Tracking Control of Unmanned Vehicles Based on Adaptive Variable Parameter MPC

Aiming at the problems of the poor trajectory-tracking performance and low control accuracy of unmanned vehicles under complex working conditions, we first estimate the lateral force of tires using the square root cubature Kalman filter (SRCKF) in order to correct the lateral stiffness of the tires online, which reduces the model bias caused by constant lateral stiffness, and then adopt a Gaussian function-based adaptive time-domain model predictive control method to improve the trajectory-tracking control accuracy of unmanned vehicles under complex working conditions. Finally, the proposed control algorithm is validated via Carsim and MATLAB/Simulink joint simulation. The results show that compared with the classical model predictive control (MPC) algorithm, the proposed control algorithm reduces the average lateral tracking error by 73.07% and the peak beta and the peak yaw rate by 50.89% and 47.51%, respectively, so that the unmanned vehicle is able to maintain good tracking performance and control accuracy.

Construction of hard BCC phases FeCrVMnTix wear-resistant high-entropy alloy coatings enhanced by solid solution of Ti elements

To address the complex working conditions encountered by 1Cr13 ferritic/martensitic steel during operation, it is imperative to develop high-entropy alloys (HEAs) protective coatings with enhanced friction resistance. In this work, guided by the valence electron concentration criterion, we constructed a single-phase body-centered cubic (BCC) structure with high hardness. This was achieved through the introduction of titanium (Ti), which has a significantly different atomic radius compared to other elements. FeCrVMnTix (x = 0, 0.5, 1.0, 1.5, and 2.0) coatings were fabricated on 1Cr13 steel surfaces using laser cladding techniques, and the microstructure, mechanical properties, and wear-resistance properties of Ti-doped HEA coatings were investigated. The results reveal that the FeCrVMnTix coatings are comprised of a BCC solid solution. Increasing Ti content refined the dendritic structure of the coatings, enhancing the Vickers hardness from 4.35 GPa to 8.27 GPa and the Young's modulus from 232.6 GPa to 273.2 GPa. Additionally, the wear rate decreased from 3.07 × 10−5 mm3·N−1·m−1 to 4.94 × 10−6 mm3·N−1·m−1. Notably, the FeCrVMnTi2.0 coating demonstrated excellent wear resistance that can be attributed to the solid solution strengthening effect of Ti. These findings underscore the pivotal role of Ti in enhancing the wear resistance of the coatings.

Effects of uneven wall thickness of steel pipes on magnetic permeability perturbation testing for internal defects

ABSTRACT Magnetic Permeability Perturbation testing (MPPT) has the advantage of high sensitivity for deeply buried defects in steel pipes. However, in complex working conditions, variations in the partial wall thickness of the pipe can affect the defect detection results. The paper discusses the influence mechanism of uneven wall thickness on the MPPT of internal defects on steel pipes and analyzes the magnetic field distribution caused by uneven wall thickness which influences on the magnetic permeability perturbation (MPP) of internal defects. The MPP is enhanced at the thinning wall and weakened at the thickening wall. The effect of uneven wall thickness on MPPT signals is confirmed through simulation and experiment. The signal of defects in thinning wall is enhanced, while the signal of defects in thickening wall is weakened. The greater the wall thickness variation, the degree of signal distortion is more significant. Experimental results show that uneven wall thickness has an effect on the detection of the defects with different depths. Finally, a method using deep saturation magnetisation is proposed to mitigate the inconsistent signals caused by uneven wall thickness. The findings will contribute to quantitative evaluation of defects in MPPT.

Molecular dynamics simulation of hybrid structure and mechanical properties of DLC/Ni-DLC thin films

To improve the mechanical properties of the rolling body surface of wind power bearings, extend its service life. In this study, a large-scale molecular/atomic parallel processor LAMMPS was introduced, and then the process of magnetron sputtering technology in the preparation of DLC/Ni-DLC thin films on the 42CrMo substrate material was simulated. The effects of deposition parameters such as sputtering temperature, sputtering voltage, deposition air pressure, and Ni doping on the residual stress, film base bonding, and organizational structure of the thin films were investigated. The simulation results show that for different deposition parameters, the atomic tensile and compressive stresses existed simultaneously in DLC/Ni-DLC films, and the residual stresses were between − 0.504–5.003 Gpa and − 2.11–0.065 Gpa, respectively; the doping of Ni effectively improved the distribution of hybrid structure and the mechanical properties of the DLC films, and the ratio of the sp3 hybrid structure in the film organization was about 2.56 times higher than that of the non-doped films, and the membrane base bonding force was increased by 32.78% and the residual stress is reduced and transitioned from tensile stress to compressive stress. In addition, it was observed that the thickness of the mixed layer of DLC/Ni-DLC films with the substrate was not increased after the thickness of the mixed layer was extended to about 2 nm. Nickel doping and reasonable control of deposition parameters help to reduce the residual stress and improve the bonding strength of the film by changing the organizational structure of the film, which provides an important theoretical and scientific basis for the preparation of low-stress, high-performance and long-life DLC films and the wide application of rolling bodies for wind power bearings under complex working conditions.

Impact and tangential composite fretting wear of Zr-4 alloy tubes under random loading conditions

The fuel rod casing tube is a crucial component in pressurized water reactor (PWR) nuclear power plants. Flow-induced vibrations in the circulating water can result in complex alternating fretting wear between the casing tube and the positioning lattice frame. Existing research on fretting wear has primarily focused on unidirectional wear, with limited investigation into composite fretting wear under complex working conditions. This study conducted impact and tangential composite fretting wear tests on Zr-4 alloy tubes under varying constant and random impact loads to examine fretting wear behavior. The findings show that the peak friction coefficients in the impact and tangential composite fretting wear tests were consistent across the three constant load conditions, with higher peak friction coefficients observed under random load conditions and a longer time required to reach these peaks during micromotion tests. Comparative analysis revealed that random impact and tangential composite fretting wear caused the most severe damage. Under constant load conditions, wear damage became more severe with increasing load, transitioning from oxidized and adhesive wear to a peeling layer as the load intensified in the impact and tangential composite fretting wear tests.

A novel antibacterial and wear-resistant strategy based on metal surface micro-texture nitriding coupled functional mesoporous silicon coating

Modified metal layers coupling durable antifouling and antibacterial properties have garnered considerable interest in various fields, including but not limited to medical equipment, kitchen utensils, public facilities, and specific industrial applications, such as transportation, energy utilization, and aerospace. Nevertheless, wear and corrosion resulting from harsh operating environments can significantly compromise the modified metal layers, leading to diminished antifouling and antibacterial capabilities. Currently, maintaining excellent antibacterial properties on the surface of metal-modified layers under complex working conditions remains a key challenge for metal protection technology. Herein, we innovatively propose a ternary collaborative strategy to optimize and extend the antifouling and antibacterial activity of metal substrate surfaces. Firstly, laser surface texturing (LST) is used to create micro-textured topography on the metal surface to serve as a reservoir for collecting debris and storing functionalized nanoparticles. Then, the surface micro-textures are treated with plasma nitriding to form a wear-resistant passivation layer that protects the aforementioned microstructures. Finally, a lubricant containing functional mesoporous silicon nanoparticles (Ag2O-MSNs@OTES) is coated on the metal surface, which not only reduces the friction coefficient but also achieves antibacterial and antifouling effects by continuously releasing silver ions from the mesoporous silicon spheres. Employing the aforementioned ternary coordination strategy, the fabricated coupling layer exhibits not only exceptional antifriction and wear properties but also remarkable antibacterial efficacy against both E. coli and S. aureus, attributed to the Ag2O-MSNs@OTES. Therefore, the wear-resisting and antibacterial coupling layer for the protection of metal surfaces has promising and versatile applications in wide areas.

MPNet: A lightweight fault diagnosis network for rotating machinery

Rotating machinery is prone to faults, especially bearing faults. Existing machinery fault diagnosis methods suffer from low accuracy and poor robustness under actual complex working conditions. To address these problems, this paper proposes a sound signal-based rotating machinery fault diagnosis method and designs a lightweight fault diagnosis network called MPNet. A multi-branch feature fusion module is constructed to capture the multi-scale correlation information of the sound features. A residual attention pyramid module is designed to adaptively learn abstract fault information at different levels, then the feature-enhanced attention maps at multi-scale generated by hierarchical fusion. Experimental results on a public bearing dataset and a self-made idler dataset reveal that the proposed method achieves 95.83% and 94.81% accuracy, respectively, meeting the requirements of lightweight and real-time detection. Compared with conventional methods, the proposed method has better diagnostic precision and robustness under strong noise. The code library is available at: https://github.com/xgli411/MPNet.

Comparative study of electric power structure of hydrogen fuel cell electric propulsion ship system

Abstract Aiming at the problem of the applicability of active and semi-active structures of hybrid energy storage units to the complex working conditions of ships in the hydrogen fuel cell electric propulsion system of ships, a comparative study of such structures is carried out in terms of the control strategy, capacity design and optimization effect, respectively. The hybrid energy storage unit in the active structure, supercapacitor and battery, are both connected to the DC bus through the converter. In the hybrid energy storage unit in the semi-active structure, the battery is connected to the low-voltage DC bus through the converter flow in parallel with the supercapacitor, and then connected to the DC bus through the converter. Two kinds of hydrogen fuel cell electric propulsion ship system topology simulation models were established by MATLAB/Simulink respectively, and simulation verification was carried out. The simulation results show that the hybrid energy storage unit with an active structure has stronger adaptability in complex working conditions during ship operation.

A forecasting model with hybrid bidirectional long short-term memory for mooring line responses of semi-submersible offshore platforms

Semi-submersible offshore platforms are often produced in the deep sea far away from land, which results in high operating costs and difficult safety maintenance due to the harsh and complex working conditions. Adopting a neural network model for online accurate prediction of mooring tension of semi-submersible offshore platforms can adjust the ballast according to the advance of mooring tension change to keep the platform in a relatively smooth movement amplitude and improve production efficiency. In addition, it can also prevent catastrophic accidents caused by anchor chain breakage due to excessive mooring tension. However, the measured mooring tension of semi-submersible offshore platforms is characterized by complex nonlinear non-stationarity, which leads to insufficient prediction accuracy of a single prediction model. This paper develops a novel online prediction model of mooring tension for semi-submersible offshore platforms based on empirical modal decomposition (EMD), convolutional neural network (CNN), and bidirectional long and short-term memory neural network (BiLSTM). The proposed model is validated using the measured data of the first semi-submersible offshore platform in the South China Sea during operation. The experimental results show that the proposed model outperforms other comparative models currently mainstream for mooring tension prediction. In addition, the proposed method can also be used to predict complex nonlinear data in the field of ship and ocean engineering.

Small data challenges for intelligent prognostics and health management: a review

Prognostics and health management (PHM) is critical for enhancing equipment reliability and reducing maintenance costs, and research on intelligent PHM has made significant progress driven by big data and deep learning techniques in recent years. However, complex working conditions and high-cost data collection inherent in real-world scenarios pose small-data challenges for the application of these methods. Given the urgent need for data-efficient PHM techniques in academia and industry, this paper aims to explore the fundamental concepts, ongoing research, and future trajectories of small data challenges in the PHM domain. This survey first elucidates the definition, causes, and impacts of small data on PHM tasks, and then analyzes the current mainstream approaches to solving small data problems, including data augmentation, transfer learning, and few-shot learning techniques, each of which has its advantages and disadvantages. In addition, this survey summarizes benchmark datasets and experimental paradigms to facilitate fair evaluations of diverse methodologies under small data conditions. Finally, some promising directions are pointed out to inspire future research.

The acquisition and calibration of coarse-grained force field parameters for graphene/nitrile rubber nanocomposites

Graphene (GNS) is an ideal reinforcing material for developing high-performance nitrile rubber (NBR) nanocomposites to withstand complex and harsh working conditions. The mesoscale molecular simulation is significant in exploring the microscopic mechanism of nanocomposites. Initially, a coarse-grained (CG) scheme for NBR was established, and the potential parameter files were obtained through the iterative Boltzmann inversion method. Then, the bond and angle parameters were fitted by harmonic potential, the non-bonded interactions were fitted by LJ 12–6 potential. The CG force field parameters were coefficients of the potential function. The parameters such as the stiffness values, potential well parameter, etc., were calibrated by stretching the NBR molecular chains, and comparing the density and stretching performances of the NBR all-atomic and CG models. A rapid method for obtaining the non-bonded parameters between NBR and GNS was proposed based on the radial distribution function curves. The non-bonded parameters were calibrated by comparing GNS pull-out simulations, and the density and tensile properties of the GNS/NBR models. Finally, the CG force field parameters applicable to the GNS/NBR nanocomposites were obtained. The study is expected to be significant for subsequent mesoscale studies on GNS/NBR nanocomposites.

Research on Key Technology of Wind Turbine Drive Train Fault Diagnosis System Based on Digital Twin

Fault diagnosis of wind turbines has always been a challenging problem due to their complexity and harsh working conditions. Although data-mining-based fault diagnosis methods can accurately and efficiently diagnose potential faults, the visibility is extremely poor. In this paper, digital twin technology is introduced into the fault diagnosis of wind turbine drive train systems, and a wind turbine drive train fault diagnosis method based on digital twin technology is proposed, which monitors and simulates the actual operating condition in real-time by establishing a digital twin model of the wind turbine drive train. In addition, an improved variational modal decomposition combined with particle swarm optimization least squares support vector machine (IVMD-PSO-LSSVM) fault diagnosis method is proposed, which not only improves the accuracy of fault diagnosis but also effectively shortens the diagnosis time and strengthens the response speed of the system. Finally, a digital twin system for condition monitoring and fault diagnosis of wind turbine drive trains is developed based on the Unity 3D platform. Experiments show that the proposed IVMD-PSO-LSSVM can accurately identify fault types with an accuracy rate of 99.1%, which is an improvement of 3.4% compared to before. The proposed digital twin model can be used for real-time monitoring of wind turbine vibration data and provide a more intuitive real-time simulation of the wind turbine’s operating status. This facilitates quick fault location and enables more accurate and efficient maintenance.

Accelerated computational strategies for multi-scale thermal runaway prediction models in Li-ion battery

Numerical simulation techniques have become an important tool in the study of thermal runaway (TR) in lithium-ion batteries (LIB). With the research progress, the complexity of the TR prediction model increases, extending its application from single battery to battery packs and entire vehicle systems. This expansion has led to a significant increase in the computational demands of TR prediction model. The computational efficiency has become a critical issue that cannot be ignored. Therefore, it is meaningful to research and develop accelerated computational strategies for the TR prediction model while ensuring the computational accuracy and stability. This paper introduces specific accelerated computational strategies to tackle this challenge. The stability and accuracy of these strategies are validated using 2D and 3D models developed with OpenFOAM software, and the speed up effect from coupling of different accelerated schemes is also analysed. The results show that, compared with the traditional prediction model, coupling various accelerated strategies can speed up the 2D model by a factor of 4.91x and 3D model by a factor of 6.07x. The optimized models with accelerated computational strategies substantially improve the ability to handle complex working conditions, large-scale models and TR propagation problems.