Arresting gear is the key equipment to ensure the safe and short-range landing of a carrier-based aircraft, which is a complex electromechanical-hydraulic system. Accurately and efficiently modeling of arresting gear system is critical to enhance its service life and safety. However, there is few researches reported that can achieve this goal at the same time, which hinders the quantitative analysis of the effects of various factors, such as velocity, mass, eccentricity etc., on the dynamic response of the arresting gear. Thus, this paper proposed an efficient model for solution of arresting gear dynamic responses based on BP neural network optimized by whale optimization algorithm (WOA-BPNN). And, based on this model a sensitivity study was carried out to quantitatively analyze the effects of various factors on the dynamic response of the arresting gear. First, a mathematical model of the arresting gear based on multibody dynamics and hydraulic system analysis was established, and its effectiveness was validated by the experiment data. Second, the dynamic response of the arresting gear under various factors was figured out by the proposed mathematical model, and a WOA-BPNN was established based on the obtained data, which could predict the dynamic response of the arresting gear with high efficiency and fidelity. Then, the Sobol global sensitivity analysis method was employed to evaluate the influence of each landing parameter on the dynamic response. Finally, the maximum cable force of the arresting gear was studied as an example of the dynamic response of the solution. According to the results, it could be concluded that the landing velocity and landing mass were the dominant factors affecting the cable force, and there were interactions among landing parameters. The proposed model could efficiently figure out the dynamic response of each component in an arresting gear and identify the most influential landing parameter, which could lay a foundation for enhancing the operational reliability of an arresting gear and optimizing the structure of the arresting gear.

As the primary power source for ships, the reliability of electric propulsion systems directly impacts the safety, stability, and economic efficiency of maritime operations. However, the composition of ship electric propulsion systems is complex and is continuously exposed to the dynamic and variable marine environment, which complicates their reliability modeling and analysis. This paper introduces a novel approach to reliability modeling for electric propulsion systems based on the Modelica language. The aim is to overcome the limitations of traditional reliability modeling methods by considering the heterogeneity, dynamicity, and interactivity of electric propulsion systems. The approach addresses system heterogeneity through multi-domain modeling, captures environmental dynamics through parametric modeling, and establishes device interactions using Modelica language connectors. Additionally, modeling efficiency is enhanced by reusing device model packages, which benefits system optimization. Using a specific ship's electric propulsion system as a case study, the modeling process and simulation results are presented to demonstrate the effectiveness and flexibility of the proposed approach. This approach offers a new tool for reliability modeling of complex electromechanical systems and contributes to enhancing the accuracy and efficiency of system reliability assessments.

The aviation electric fuel pump, characterized as a multidomain complex system, exhibits a highly integrated functional structure that complicates traditional design methods, which often fail to adequately address the interdependencies among various components. This paper proposes a model-based-systems-engineering-driven design method for aviation electric fuel pumps, with a focus on multidomain collaborative design and cross-domain performance optimization. The method incorporates both causal and noncausal modeling techniques and conducts a comprehensive analysis of the system’s multidomain attributes. It comprises two phases: forward design and backward verification. The forward design phase utilizes causal and noncausal modeling techniques to collaboratively analyze system requirements, functionality, and physical characteristics, ensuring information integrity, consistency, and accuracy throughout the system design process. The backward verification phase evaluates the coupled performance based on the cross-domain propagation effect within the established causal and noncausal frameworks, allowing for design optimization through the adjustment of critical parameters. Finally, the effectiveness of the proposed method is demonstrated through its application to the design and optimization of an aviation electric fuel pump. The resulting design scheme is validated to fulfill the specified requirements, and key parameters, including the modulus of the gear pump and the number of teeth, are identified for optimal design outcomes.

To address the limitation in current reliability assessments of carrier-based aviation support systems, which often neglect the bidirectional failure coupling between aircraft and support resources, this paper proposes a dual-failure coupling reliability modeling method based on system dynamics (SD). First, the boundaries and core elements of reliability evaluation are defined. Focusing on a specific aircraft failure mode, a three-level SD framework of “operational - faulty - under repair” states is established for both aircraft and aviation support system. Next, by incorporating the failure characteristics of both aircraft and support equipment, along with practical constraints on support resources, the method further develops formulations for failure flow rates, repair flow rates, and resource adequacy coefficients. These formulations enable a precise characterization of how aircraft failures impose additional loads on support equipment and how equipment failures, in turn, constrain aircraft repair processes, thereby quantifying the coupled effects on overall support capability. Finally, the model’s effectiveness is validated through comparative simulations in AnyLogic, contrasting independent-failure and coupled-failure scenarios. The results illustrate the reliability evolution under aircraft-support system interactions and provide quantitative support for the optimized allocation of resources in carrier-based aviation support systems.

Advanced arresting gear (AAG) is essential equipment for ensuring the safe, short-range landing of aircraft. As a complex electromechanical-hydraulic system, it is susceptible to numerous failure modes. In this paper, a comprehensive system model of an advanced arresting gear was proposed. The model’s effectiveness and accuracy were validated against existing experimental data. Subsequently, the failure modes of key AAG components were analyzed, and their influence on the system’s performance is investigated using the system model. Simulation results were used to analyze the changes in aircraft displacement and velocity, as well as the arresting cable force, under various failure conditions. The results provide a theoretical basis and data support for the design optimization and fault diagnosis of AAG systems.

The grid-stiffened structure is the key load-carrying sections of launch vehicles such as the storage tank and the interstage section. Thus, its load-carrying performance is directly related to the load-carrying efficiency of a rocket. In this paper, a structural optimization method based on the load-carrying efficiency was proposed for grid-stiffened structures. Firstly, a general 3D parametric geometrical modeling method for grid-stiffened structure was proposed, which could optimize the position of stiffeners and incorporate the key geometric features such as welds. Based on this method, the buckling load of the structure considering the internal pressure was figured out by finite element analysis, and the solution accuracy was verified. Then, a surrogate model was constructed based on the Particle Swarm Optimization-Least Squares Support Vector Regression algorithm for accurately and efficiently predicting the buckling load of grid-stiffened structures. Subsequently, the sensitivity of each structural parameter to the buckling load was evaluated using the Sobol method to identify the key ones. Finally, targeting these key parameters, a load-carrying efficiency optimization model was proposed, considering constraints such as mass and geometric constraints. The results showed that the load-carrying efficiency of the optimized structure increased by 11.20%. The methods proposed in this paper could efficiently establish a practical 3D geometrical model of the grid-stiffened structure, effectively improve its load-carrying efficiency, and maintain the given parameters within the specified ranges. This approach could lay a foundation for reducing the launch costs and improving the transportation efficiency.

The turbojet engine, as a crucial power unit for specific aircraft, holds great significance in reliability research. However, the engine's multi-domain coupling and multi-fault nature pose challenges for reliability analysis. This paper proposes a reliability analysis method for turbojet engine system under multiple fault conditions based on a multi-domain unified model. Firstly, the method of describing the change of turbojet engine system performance based on mathematical model is used to analyze system operating principles. Secondly, the performance model of the turbojet engine system in the integrated system realizes the multi-domain coupling, and the multi-domain unified model of the system is utilized to simulate the parallelism of multiple faults as well as the superimposed influence on each other. And finally, the model is subjected to the simulation as well as the reliability analysis to solve the deficiencies of the traditional methods. Taking a certain aircraft turbojet engine system as an example, after modeling, fault injection, Monte Carlo simulation and data processing, the cumulative distribution function of system fault is calculated, which provides a new method for turbojet engine reliability research.

Aiming at the problems that traditional condition-based maintenance methods inadequately analyze the impacts of failure modes and maintenance scenarios, leading to issues such as maintenance resource waste and cost increases, the paper proposes a logical decision-making method for condition-based maintenance that takes the failure consequences into account, which is aimed at degradable equipment whose operational status can be detected. By classifying the impacts on personnel safety, task availability, and maintenance costs, failure consequence models are established, including a safety model, a task model, and an economic model. Furthermore, maintenance logic decisions are made based on the failure consequence model to determine logical decision-making models for different maintenance scenarios, and to calculate the optimal maintenance level and timing for the equipment. The proposed method addresses the issue of inadequate analysis regarding the repercussions of failures in condition-based maintenance. It facilitates the determination of optimal maintenance timing based on the consequences of such failures , thereby enabling precise and efficient maintenance of equipment across various scenarios.

Trochoidal machining could significantly improve cutting efficiency, enhance cutting stability, reduce cutting temperature, extend tool life, and reduce the cutting costs. However, in trochoidal machining, there are few studies focusing on modelling the instantaneous cutting power due to overlooking the importance of cutting temperature modelling. Also, instantaneous cutting power is an important basis for the optimization of trochoidal parameters and cutting parameters. In this work, we established a new and efficient method that could predict the instantaneous cutting power in trochoidal machining in high fidelity. First, the specific cutting energy of a given workpiece material, cutting tool, and cutting parameter in milling process was calibrated by cutting experiments. Second, the influence of the radial depth of cut on the specific cutting energy in milling process was quantitatively studied. Third, combining the obtained relationship between the specific cutting energy and radial depth of cut, the specific cutting energy curve in trochoidal machining was obtained. Then, a way to figure out the instantaneous material removal rate was proposed based on the acquired instantaneous 3D un-deform chip in trochoidal machining. Finally, based on the obtained specific cutting energy and instantaneous material removal rate, an accurate and efficient approach to predicting the instantaneous cutting power in trochoidal machining was proposed, and a practical application was demonstrated. The effectiveness of the proposed approach was validated by cutting experiments. The method proposed in this work could be adopted in cutting parameter optimization, tool-path optimization, and cutting temperature prediction in trochoidal machining.

Due to the insufficient feature learning ability and the bloated network structure, the gear fault diagnosis methods based on traditional deep neural networks always suffer from poor diagnosis accuracy and low diagnosis efficiency. Therefore, a small channel convolutional neural network under the multiscale fusion attention mechanism (MSFAM-SCCNN) is proposed in this paper. First, a small channel convolutional neural network (SCCNN) model is constructed based on the framework of the traditional AlexNet model in order to lightweight the network structure and improve the learning efficiency. Then, a novel multiscale fusion attention mechanism (MSFAM) is embedded into the SCCNN model, which utilizes multiscale striped convolutional windows to extract key features from three dimensions, including temporal, spatial, and channel-wise, resulting in more precise feature mining. Finally, the performance of the MSFAM- SCCNN model is verified using the vibration data of tooth-broken gears obtained by a self-designed experimental bench of an ammunition supply and delivery system.

This paper proposes an improved FMEA method that integrates component importance and Fuzzy-TOPSIS. The aim is to overcome the limitations of traditional FMEA methods, such as the significant subjective influence in determining risk factors O, S, and D, and the lack of consideration for the weights among risk factors. Notably, this is the first instance where component importance is incorporated into the FMEA method, thereby enhancing the precision of system failure mode ranking. The proposed method is applied to a water supply system, presenting the analysis process and results. The effectiveness and accuracy of the method are demonstrated through comparisons with other approaches. This method offers fresh perspectives for FMEA analysis, elevating the scientific rigor and precision of system FMEA analysis.

The electric propulsion system of a ship represents a quintessential electromechanical system, the reliability of which is crucial to assess. This paper introduces a novel approach for modeling the reliability of electric propulsion systems using the Modelica language, designed to overcome the shortcomings of traditional reliability modeling methods that often fail to account for the heterogeneity, dynamicity, and interactivity inherent in electric propulsion systems. This approach utilizes multi-domain modeling to address system heterogeneity, employs parametric modeling to capture environmental dynamics, and facilitates device interactions through the connectors provided by the Modelica language. Additionally, modeling efficiency is enhanced through the reuse of the device model packages. An electric propulsion system from a specific vessel is used as a case study to demonstrate the modeling process and simulation results, validating the effectiveness and adaptability of the proposed method. This approach introduces a novel tool for the reliability modeling of complex electromechanical systems, thereby enhancing the precision and efficiency of system reliability assessments.

The special spatial structure and the long-term harsh working environment make the transient response of wire rope complicated, so accurate assessment and prediction of its mechanical characteristics are of great significance to ensure the safe and stable operation of related equipment. In this paper, a method is proposed for the analysis of the mechanical characteristics of a 6 × K31WS + FC wire rope with or without wire breakage. Firstly, an accurate parametric geometric model of the wire rope is established based on the Frenet frame method, the material properties of the wire rope are acquired by experiments, and the finite element model of the wire rope is built. Then, a mechanical model of the wire rope is proposed to verify the validity of the finite element model. Finally, the influences of the number, distribution, and location of the broken wires on the mechanical characteristics of the wire rope are thoroughly analyzed. This work proposes a comprehensive framework that can quantitatively analyze the mechanical characteristics of the complex wire rope with or without wire breakage, which provides a practical method for its reliability and maintenance evaluation and has guiding significance for the shape design and structural optimization of the wire rope.

Owing to the large size of the skin-stringer structure of a launch vehicle, the manufacturing cost is high, the test period is long, and thus the test sample size is generally small. However, the load-bearing performance of a skin-stringer structure must be tested multiple times before it can be used in practice. To address these problems, in this study, first, a reduced-scale test specimen was designed according to similar principles and detailed geometric parameters of the designed model were obtained. Second, based on a buckling analysis performed using ABAQUS, the deformation and stress distributions of the prototype and the reduced-scale model were compared, and similarities of both deformation and stress distributions of the prototype and the reduced-scale model were proved. Third, a sensitivity study was conducted using a Kriging agent model to determine the key parameters that significantly influence the ultimate bearing capacity of both the prototype and reduced-scale model. Finally, based on the above analysis, the similarities of the stress distributions, deformation distributions, and bearing capacity influencing factors of the prototype and the reduced-scale model were verified. This study provides a basis for the design and similarity evaluation of a reduced-scale model of a skin-stringer structure. It also lays a foundation for testing large structures based on reduced-scale structures.

Phased Mission System (PMS) is widely used in large complex equipment such as spacecraft and satellites. Due to sophisticated structure and complex missions, the components and systems of the large complex equipment have multiple failure modes which are correlated. On the one hand, taking multiple correlated failure modes in to consideration can describe the multi-state characteristics of the system more accurately and decouple the correlation between different phases of the system more clearly. On the other hand, its complexity makes the reliability evaluation of PMS more difficult. To solve this problem, this paper discusses the modeling method of multiple failure modes and their correlation, and proposes an improved combination method of Multiple Failure Mode Tree (MFMT) and Multi-state and Multi-valued Decision Diagram (MMDD) model, completes the reliability evaluation of PMS in cross-level (failure mode-component-system) and cross-scale (single phase-multiple phase). Finally, the MFMT-MMDD model of the satellite attitude orbit control system (AOCS) is established and Monte Carlo simulation is used to verify the feasibility and accuracy of this modeling method.

Due to the insufficient feature learning ability and the bloated network structure, the gear fault diagnosis methods based on traditional deep neural networks always suffer from poor diagnosis accuracy and low diagnosis efficiency. Therefore, a small channel convolutional neural network model under the convolutional block attention module (CBAM-SCCNN) is proposed in this paper. Firstly, a small channel convolutional neural network (SCCNN) model is constructed based on the framework of the traditional AlexNet model in order to lightweight the network structure and improve the learning efficiency. Then, the convolutional block attention module (CBAM) is embedded into the SCCNN model to extract the "what" and "where" information of important features from the channel and spatial dimensions respectively to enhance feature recognition ability and improve the accuracy of the model. Finally, the diagnostic performance of the CBAM-SCCNN model is verified using the vibration data of tooth-broken gears, which were obtained by an own-designed experimental bench of the swing arm gear from a naval gun loader. The findings demonstrate that the proposed model improves fault diagnosis accuracy while significantly reducing diagnostic time.

Dynamic Distributed Cooperative Systems ( DDCSs ) are complex repairable systems with the characteristics of distribution, dynamism, and cooperation, which makes it difficult to evaluate their mission reliability. Traditional reliability analysis methods either simplify the system or face the problems such as dimensional curses and space explosions. To address this issue, the paper proposed a multi-agent based modeling and simulation method. The method can first construct multiple agents to decompose the "distributed" characteristics of systems, and secondly define the state transfer of agents to describe the "dynamic" characteristics during operation, and then determine the communication rules between agents to simulate the "cooperative " characteristics during mission process and achieve the mission reliability evaluation. At last, an aviation security system of an aircraft carrier is taken as a case study, the feasibility, flexibility, and strong solving ability of the proposed method are demonstrated by evaluating its mission reliability.

In gear fault diagnosis, most current intelligent fault diagnosis methods show good classification performance for fault pattern recognition. However, when detecting fault severity, the difficulty of diagnosis is increased due to the high similarity between the monitoring signals, which requires improving the sensitivity, stability, and accuracy of diagnosis methods. To address this issue, a parameter-optimized deep belief network (DBN) based on sparrow search algorithm (SSA) is proposed for gear fault severity detection. Firstly, the initial DBN is trained by the labeled gear fault signals in different severities. Secondly, SSA is introduced to optimize the learning rate and the batch size of the initial DBN, so as to avoid the interference caused by selecting network parameters by subjective experience. Finally, the detection method of gear fault severity based on the improved DBN with the optimal parameter combination is constructed. The performance of the proposed method is evaluated by analyzing the gear datasets under five degrees of tooth-breaking fault, the results show that the average detection accuracy reaches over 96% with a standard deviation of 1.46%. Compared with other methods, it is proved that the proposed method has better feature extraction ability, stability, and accuracy for gear fault severity detection.