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The purpose of this paper is to recognize and label the faults in wind turbines with a new density-based clustering algorithm, named contour density scanning clustering (CDSC) algorithm.

The algorithm includes four components: (1) computation of neighborhood density, (2) selection of core and noise data, (3) scanning core data and (4) updating clusters. The proposed algorithm considers the relationship between neighborhood data points according to a contour density scanning strategy.

The first experiment is conducted with artificial data to validate that the proposed CDSC algorithm is suitable for handling data points with arbitrary shapes. The second experiment with industrial gearbox vibration data is carried out to demonstrate that the time complexity and accuracy of the proposed CDSC algorithm in comparison with other conventional clustering algorithms, including k-means, density-based spatial clustering of applications with noise, density peaking clustering, neighborhood grid clustering, support vector clustering, random forest, core fusion-based density peak clustering, AdaBoost and extreme gradient boosting. The third experiment is conducted with an industrial bearing vibration data set to highlight that the CDSC algorithm can automatically track the emerging fault patterns of bearing in wind turbines over time.

Data points with different densities are clustered using three strategies: direct density reachability, density reachability and density connectivity. A contours density scanning strategy is proposed to determine whether the data points with the same density belong to one cluster. The proposed CDSC algorithm achieves automatically clustering, which means that the trends of the fault pattern could be tracked.

The purpose of this paper is to propose a comprehensive framework for integrating big data analytics (BDA) into cyber-physical system (CPS) solutions. This framework provides a wide range of functions, including data collection, smart data preprocessing, smart data mining and smart data visualization.

The architecture of CPS was designed with cyber layer, physical layer and communication layer from the perspective of big data processing. The BDA model was integrated into a CPS that enables managers to make sound decisions.

The effectiveness of the proposed BDA model has been demonstrated by two practical cases − the prediction of energy output of the power grid and the estimate of the remaining useful life of the aero-engine. The method can be used to control the power supply system and help engineers to maintain or replace the aero-engine to maintain the safety of the aircraft.

The communication layer, which connects the cyber layer and physical layer, was designed in CPS. From the communication layer, the redundant raw data can be converted into smart data. All the necessary functions of data collection, data preprocessing, data storage, data mining and data visualization can be effectively integrated into the BDA model for CPS applications. These findings show that the proposed BDA model in CPS can be used in different environments and applications.

As arc suppression coils (ASCs), magnetically controlled reactors (MCRs) are usually operated in the single-phase mode. Due to the lack of a third order harmonic compensation circuit, the current harmonics are high. The purpose of this paper is to propose a novel structure of MCR and a genetic algorithm (GA) to determine the parameters which will result in minimum total harmonics.

This paper proposes the structure and the working principle of the multi-valve controlled saturable reactor (MCSR). There are several sorts of magnetic valves in the iron cores of the MCSR. The saturation degree of each magnetic valve is different when the same direct component of the magnetic flux is generated in the iron core, therefore current harmonics of different phases emerging, i.e. the total harmonics can be reduced. The magnetization characteristics and the mathematical model of the current harmonics of the MCSR are presented by introducing three parameters. The optimal values of the parameters that result in the smallest total harmonic distortion in the output current are calculated by a GA.

The simulation and experimental results are coincident with the theoretical analyses, which prove the effectiveness of the proposed method on harmonic suppression.

The method proposed in this paper can successfully reduce the current harmonics of the conventional MCR, including but not limited to the ASC. A prototype MCSR (540 kVA/10 kV) has been designed and constructed.

In this paper, a MCSR is proposed. The mathematical model of the MCSR for harmonic analysis is developed. The optimal parameters that result in the smallest THD in the output current are calculated. The mathematical model can be also used for the harmonic analysis of conventional MCRs.

This study aims to clarify the evolution law of stress field and fracture field during the mining process of inclined coal seam, to prevent the occurrence of roof burst water and impact ground pressure accident during the advancing process of working face.

The evolution law of stress-fracture field under different mining conditions of inclined coal seam was studied by using discrete element method and similar material simulation method.

The overburden stress at the lower end of the coal seam was mainly transmitted to the deep rock mass on the left side, and the overburden stress at the upper end was mainly transmitted to the floor direction. With the increase of the inclined length of the mining coal seam, the development of the fracture zone gradually evolves from the “irregular arch” form to the “transversely developed trapezoid” form. The development range of the fracture zone was always in the internal area of the stress concentration shell.

An original element of this paper is based on the condition that the dip angle of coal seam is 35°, and the evolution law of overburden stress-fracture field during the excavation of coal seam with different lengths was analyzed by UDEC numerical simulation software. The coupling relationship between stress shell and fracture field was proposed, and the development range of fracture zone was determined by stress. The value of this paper is to provide technical support and practical basis for the safety production of a mine working face.

The aim of this paper is to implement direct teaching of industrial manipulators using current sensors. The traditional way to implement teaching is either to use a teaching pedant, which is time consuming, or use force sensors, which increases system cost. To overcome these disadvantages, a novel method is explored in the paper by using current sensors installed at joints as torque observers.

The method uses current sensors installed at each joint of a manipulator as torque observers and estimates external forces from differences between joint-driven torque computed based on the values of current sensors and commanded values of motor-driven torque. The joint-driven torque is computed by cancelling out both pre-calibrated gravity and friction resistance (compensation). Also, to make the method robust, the paper presents a strategy to detect unexpected slowly drifts and zero external forces and stop the robot in those situations.

Experimental results demonstrated that compensating the joint torques using both pre-calibrated gravity and friction resistance has performance comparable to a force sensor installed on the end effector of a manipulator. It is possible to implement satisfying direct teaching without using force sensors on 7 degree of freedom manipulators with large mass and friction resistance.

The main contribution of the paper is that the authors cancel out both pre-calibrated gravity and friction resistance to improve the direct teaching using only current sensors; they develop methods to avoid unsafe situations like slow drifts. The method will benefit industrial manipulators, especially those with large mass and friction resistance, to realize flexible and reliable direct teaching.

The purpose of this paper devoted to the application of modal analysis to analyze the flow structure of trailing edge cutback film cooling and the effects of vortex structure on the film cooling effectiveness of the cutback surface.

Large eddy simulation (LES) is used to simulate the trailing edge cutback film cooling. The results of LES are analyzed by proper orthogonal decomposition (POD) method and dynamic mode decomposition (DMD) method. The POD method is used to determine the dominated vortex structure and the energy level of these structures. The DMD method is used to analyze the relationship between vortex structures and wall temperature.

The POD method shows that the flow field consists of three main vortices – streamwise vortex, lip vortex and coolant vortex. The DMD results show that the lip vortex mainly acts on the middle section of the cutback surface, while the streamwise vortex mainly acts on the back section of the cutback surface.

The modal analysis is only based on numerical simulation but the modal analysis of experimental results will be further studied in the future.

This paper presents the powerful ability of the modal analysis method to study complex flows in trailing edge cutback film cooling. Establishing the relationship between vortex and wall temperature by modal analysis method can provide a new idea for studying convective heat transfer problems.

The role of streamwise vortex in the flow of the trailing edge cutback cooling and its effect on the cooling effectiveness of the cutback surface is found.

Using the conservation of resources (COR) theory, the present study aims to examine the role of participative leadership in frontline service employees (FLEs)’ service recovery performance. The present study also tests FLEs’ role breadth self-efficacy (RBSE) as a theoretically relevant mediator and FLE trait mindfulness as an important moderator.

Data were collected using time-lagged (three rounds, two weeks apart) from two sources (193 FLEs and 772 customers, who experienced a service failure). Structural equation modeling (Mplus, 8.6) was employed to analyze the data.

The results revealed that participative leadership was positively associated with FLEs service recovery performance, both directly and indirectly, via RBSE. The results also showed that FLE trait mindfulness moderated the link of participative leadership with RBSE and the indirect association of participative leadership with service recovery performance, via RBSE.

This study suggests that organizational leaders who exhibit participative leadership behavior are valuable for organizations. By demonstrating such behaviors, they boost FLEs' RBSE, which in turn improves their service recovery performance.

The present work makes important contributions to the literature on service recovery performance by foregrounding two important yet overlooked antecedents (participative leadership and RBSE) of FLE service recovery performance. The present work also contributes to the nascent literature on the antecedents and outcomes of RBSE in service contexts.

The purpose of this paper is to solve the problem that mobile robots are still based on reactive collision avoidance in unknown dynamic environments leading to a lack of interaction with obstacles and limiting the comprehensive performance of mobile robots. A dynamic window approach with multiple interaction strategies (DWA-MIS) is proposed to solve this problem.

The algorithm firstly classifies the moving obstacle movement intention, based on which a rule function is designed to incorporate positive incentives to motivate the robot to make correct avoidance actions. Then, the evaluation mechanism is improved by considering the time cost and future information of the environment to increase the motion states. Finally, the optimal objective function is designed based on genetic algorithm to adapt to different environments with time-varying multiparameter optimization.

Faced with obstacles in different states, the mobile robot can choose a suitable interaction strategy, which solves the limitations of the original DWA evaluation function and avoids the defects of reactive collision avoidance. Simulation results show that the algorithm can efficiently adapt to unknown dynamic environments, has less path length and iterations and has a high comprehensive performance.

A DWA-MIS is proposed, which increases the interaction capability between mobile robots and obstacles by improving the evaluation function mechanism and broadens the navigation strategy of DWA at a lower computational cost. After real machine verification, the algorithm has a high comprehensive performance based on real environment and provides a new idea for local path planning methods.

This study aims to optimize control strategies for multi-unmanned aerial vehicle (UAV) systems by integrating differential game theory with sliding mode control and neural networks. This approach addresses challenges in dynamic and uncertain environments, enhancing UAV system coordination, operational stability and precision under varying flight conditions.

The methodology combines sliding mode control, differential game theory and neural network algorithms to devise a robust control framework for multi-UAV systems. Using a nonsingular fast terminal sliding mode observer and Nash equilibrium concepts, the approach counters external disturbances and optimizes UAV interactions for complex task execution.

Simulations demonstrate the effectiveness of the proposed control strategy, showcasing enhanced stability and robustness in managing multi-UAV operations. The integration of neural networks successfully solves high-dimensional Hamilton–Jacobi–Bellman equations, validating the precision and adaptability of the control strategy under simulated external disturbances.

This research introduces a novel control framework for multi-UAV systems that uniquely combines differential game theory, sliding mode control and neural networks. The approach significantly enhances UAV coordination and operational stability in dynamic environments, providing a robust solution to high-dimensional control challenges. The use of neural networks to solve complex Hamilton–Jacobi–Bellman equations for real-time multi-UAV management represents a groundbreaking advancement in autonomous aerial vehicle research.

This paper aims to carry out the research on the influence of ground effect on the performance of robotic fish propelled by oscillating paired pectoral fins.

The two-dimensional ground effect model of the oscillating pectoral fin without considering flexible deformation is established by introducing a two-dimensional fluid ground effect model. The parameters of the influence of ground effect on the oscillating pectoral fin are analyzed. Finally, the ground effect test platform is built, and a series of hydrodynamic experiments are carried out to study the influence of ground effect on the propulsion performance of the robotic fish propelled by oscillating paired pectoral fins under different motion parameters.

The thickness of the trailing edge and effective clearance are two important parameters that can change the influence of ground effect on the rigid pectoral fin. The experimental results are consistent with that obtained through theoretical analysis within a certain extent, which indicates that the developed two-dimensional ground effect model in this paper can be used to analyze the influence of ground effect on the propulsion performance of the oscillating pectoral fin. The experiment results show that the average thrust increases with the decreasing distance between the robot fish and the bottom. Meanwhile, with the increase of oscillation frequency and amplitude, the average thrust increases gradually.

The developed two-dimensional ground effect model provides the theoretical basis for the further research on the influence of ground effect on the propulsion performance of the oscillating pectoral fin. It can also be used in the design of the bionic pectoral fins.