Introduction Bearing fault detection and prevention are crucial. However, traditional diagnostic methods generally suffer from insufficient accuracy when dealing with complex bearing faults. Therefore, developing new methods that can effectively characterize complex fault features and achieve high-precision diagnosis has significant theoretical and engineering value.Methods This study proposes a vibration image generation method based on Empirical Mode Decomposition-Adaptive Angle Distribution Polar Image (EMD-AADPCI) and constructs a hybrid diagnostic model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). First, the vibration signal is processed by Empirical Mode Decomposition, and the designed adaptive angle distribution mechanism dynamically allocates polar coordinate angles according to the local features of the intrinsic mode functions, converting the signal into a two-dimensional vibration image containing rich fault information. Subsequently, a CNN-LSTM hybrid model is constructed. CNN extracts spatial and deep features from the image, and LSTM captures the temporal dependencies between features, ultimately achieving accurate classification of complex bearing faults.Results Experiments show that the proposed method significantly outperforms traditional methods. In terms of feature representation, the vibration images generated by EMD-AADPCI achieved a 20.25% improvement in fault classification accuracy compared to the comparative method MIC-SPCI (reaching 93.00%). The constructed CNN-LSTM model achieved a training accuracy of 94.88% with a loss rate as low as 1.43%. In the composite fault diagnosis task, the model achieved a classification accuracy of 98.00%. After 10 repeated experiments, the model achieved average accuracy, recall, and F1 score of 98.13%, 98.72%, and 98.33% for different composite fault diagnoses, respectively. Even in a low signal-to-noise ratio environment with strong noise interference (-4 dB), the model maintained a diagnostic accuracy of over 97%, demonstrating good robustness.Discussion The proposed EMD-AADPCI method can more effectively preserve and highlight fault-related information, while the CNN-LSTM hybrid model fully leverages the advantages of spatial feature extraction and time series modeling. Experimental results show that this method has extremely high accuracy and anti-interference ability in bearing composite fault diagnosis. This provides an effective and innovative solution for intelligent diagnosis and preventive maintenance of complex faults in bearings and other rotating machinery, and has good prospects for widespread application.

The four-rotor plant protection Unmanned Aerial Vehicle (UAV) is an important piece of equipment for the efficient plant protection field. However, the flight-coupled wind field is unclear, which has become the bottleneck factor limiting the improvement of the spray quality. When the multi-rotor plant protection UAV flew and sprayed, the liquid droplets were accelerated, the stems and branches were shaken, and the leaves were turned over in the disturbance area of the spiral backward flight-coupled wind field. The flight-coupled wind field, liquid droplets and crop canopy were closely related. Only when the flight-coupled wind field was analyzed could the interaction mechanism among the flight-coupled wind field, liquid droplets, and crop canopy be effectively studied, then the flight and spray scheme could be reasonably formulated. By combining the Re-Normalization Group (RNG) k - ε turbulence model, compressible Reynolds Averaged Navier-Stokes (RANS) equation, the dynamic mesh based on the spring smoothing and layering method, pressure-velocity coupling algorithm, a computational fluid dynamics (CFD) model of the flight-coupled wind field for the four-rotor plant protection UAV was established, the dynamic evolution law of the flight-coupled wind field in the spatial and temporal dimensions was also discussed in this paper. Numerical simulations were carried out for flight-coupled wind fields in two working conditions, analysis showed that the maximum relative error between the simulated and measured values of Z b -direction ( Z direction in the absolute coordinate system) velocity was less than 12.7% when the flight-coupled wind field was stable. When switching from hover to flight state, the downwash wind field experienced a lateral interruption and developed into the stable flight-coupled wind field after flying for 0.696 s, and the velocity distribution diagram of the cross section evolved from four rings to four horseshoe vortices. When the flight-coupled wind field evolved to stabilize, the dense atmosphere reduced the absolute values of the four Z b -direction velocity peaks at observation line 1 from left to right along the flight direction by 4.5%, 4.2%, 9.0%, and 26.1% respectively. The angles between the horizontal direction and the four curves formed by Z b -direction velocity peaks were also changed from 90° to 72°, 69°, 61°, and 56° respectively at the flight speed of 3 m/s. Therefore, the installation of the nozzle and the formulation of the spray strategy are crucial to improve the spray deposition effect.

This article focuses on the motion control issue for uncertain robotic manipulators with unknown nonlinear dynamics and exogenous disturbances simultaneously via the output position measurement only. In detail, the extended state observer is employed to obtain the estimations of immeasurable system state information and lumped disturbances simultaneously. Meanwhile, the multilayer neural network with good approximation performance is incorporated into the observer-controller scheme to identify the unknown nonlinear dynamics. As a result, both large nonlinear dynamics and strong lumped disturbances can be compensated feedforwardly. Significantly, prescribed tracking performance and asymmetric time-varying state constraints can be realized simultaneously.

A single-rotor UAV fitted with a pulse-jet thermal fogging machine was developed. Computational fluid dynamics (CFD) was employed to simulate the downwash airflow and fog distribution of a single-rotor UAV fitted with a pulse-jet thermal fogger. The developed CFD models were validated in three steps by comparing the calculated results with the measurement experiments. Predicted air velocities of the single-rotor UAV downwash airflow agreed well with measured velocities. The model was also able to predict the fog droplet deposition density of the pulse-jet thermal fogger alone, and fitted to the single-rotor UAV, with the relative errors within 20% and 30%, respectively. The validated CFD model was then employed to investigate the effects of the headwind, crosswind and UAV operating height on the downwash airflow and the fog distribution. Results indicated that when the pulse-jet thermal fogging machine was mounted on the UAV, crosswinds needed to be avoided. At the UAV flight speed of 2 m s(-1) and natural wind speed of 1 m s(-1), a modest headwind appearance could help improve thermal fogging efficiency at different operating heights. The results have important research value and practical significance for improving the pesticide efficiency of UAV sprayers.

Multi-rotor plant protection Unmanned Aerial Vehicles(UAV)has good terrain adapt ability and efficient ultra-low altitude spraying capacity,which is one of the important development directions of efficient plant protection equipment.However,the interaction mechanisms of wind field,droplet and crop under flight operation are unclear,which has become the bottleneck restricting the improvement of the deposition distribution quality.This study aims to analyze the problems such as insufficient correlation studies and unclear deposition mechanism of various elements in the spray application of plant protection UAV.This study focuses on the whole process of generation,settlement,deposition and drift of pesticide droplet.And based on the previous research,this paper discussed the research status of spray pesticide liquid with nozzle,the interaction between the wind field and the droplet,as well as the coupling of the wind field,the droplet and the canopy.The effect of nozzle atomization on spray effect,the distribution law of wind field under hover and flight conditions of plant protection UAV,the distribution law of droplets deposition and drift in canopy under flight spary conditions were analyzed separately.Based on the above discussion,the application background,research progress and empirical models of aviation application technology at home and abroad were reviewed.Some problems that need to be solved in the spary application process of multi-rotor plant protection UAV were identified.The matching and relationships between nozzle selection(hydraulic nozzle,centrifugal nozzle,etc.)and working parameters(pressure,speed,Angle,flow,etc.),UAV flight parameters(flight speed,rotor rotation speed,etc.),pesticide characteristics(density,viscosity,surface tension,etc.)and initial parameters of liquid droplets(particle size distribution,initial velocity)are not clear,a suggestion was proposed to establish a nozzle selection decision-making expert system for multi-rotor plant protection UAV spray scene.In terms of the coupling effect of flight combination wind field(rotor downwash flow,upwind flow,natural cross wind),pesticide liquid droplets(physical property parameters after atomization),crop canopy(Stems,branches,leaves of a typical growth cycle of a crop),field environment(temperature,humidity)and operation parameters(flight speed and altitude),a proposal was put forward to establish a three-phase coupling model of combined wind field-pesticide liquid droplet-crop canopy based on scientific assumptions of deposition effects.Finally,the future development of application technology of Multi-rotor plant protection UAV was prospected.

Planning the temporary takeoff/landing site's location (TTSL) is an essential task faced by helicopter spraying pesticide operations in the forest. However, neither a scientific theoretical model nor a planning method do exist to address this issue. In this study, a mathematical model for a helicopter's TTSL planning is constructed, and an intelligent fusion algorithm (IWOA-IACO-CLC) is proposed based on the improved whale optimization algorithm (IWOA), improved ant colony optimization (IACO) algorithm, and combinatorial logic classifier (CLC). The fusion algorithm first adds a memory function and Le ' vy flight disturbance strategy to the whale optimization algorithm, which is used to enhance the global search capability of TTSL. Subsequently, the movement strategy of IACO is modified to a lateral movement within the forest and longitudinal movement between forests to calculate the fitness of the IWOA. Finally, the CLC is used to control the feasible domain of TTSL based on the idea of penalty function. In this setting, five Chinese planted forests were employed for route planning experi-ments, and the dispatch route length, spraying route length, and extra coverage rate obtained by the intelligent fusion algorithm and the artificial empirical method were analyzed. The results reveal that the IWOA-IACO-CLC algorithm achieves the optimal solution, and the five planned TTSL are more reasonable than those obtained by the artificial empirical method. The dispatch route is shortened by 13.26%, 3.27%, 13.40, 39.67% and 30.47%, the spraying route is shortened by 1.94%, 0.22%, 0.47%, 0.91% and 0.28%, and the extra coverage rate is decreased by 29.15%, 24.92%, 14.63%, 25.99% and 8.24%, respectively. Furthermore, the proposed mathe-matical model and intelligent fusion algorithm achieve the TTSL planning, while the rational TTSL planning reduces the dispatch route length and the operation cost. This study provides a theoretical basis for the TTSL planning of aerial spray helicopters, as well as a technical reference for the realization of smart forestry.

Relying on the pilot's flight experience to plan a temporary helicopter takeoff/landing site location, dispatch routes, and spraying routes often results in very long dispatch routes, imprecise forest area coverage, and a high extra coverage rate. An intelligent fusion algorithm was proposed to address these problems. A vector modeling method (VMM) was proposed to determine the optimum spraying route with the minimum number of turnarounds and the lower extra coverage rate. An improved genetic algorithm (IGA) was used to plan the dispatch route. Subsequently, the improved SSA (ISSA) was utilized to plan the takeoff/landing site location. An artificial neural network (ANN)-based classifier was constructed to constrain the search domain of the ISSA to avoid ponds, lakes, and other areas unsuitable for takeoff/landing. The proposed intelligent fusion algorithm is called VMM-ISSA-IGA-ANN. The planning of the takeoff/landing site locations, dispatch routes, and spraying routes for helicopter plant protection operations was carried out in four forested environments in Zhengzhou, Kaifeng, Mingguang, and Jurong, China. The planning scheme was compared with that of an aviation company. The results showed that the takeoff/landing site location derived from the VMM-ISSA-IGA-ANN was more reasonable than that obtained by the aviation company. The dispatch route was shortened by 6.51%, 23.7%, 42.07%, and 49.80%, the spraying route was shortened by 1.94%, 0.22%, 0.05%, and 0.91%, the extra coverage rate was reduced by 29.15%, 24.92%, 14.46%, and 26.04%, and the number of turnarounds was reduced by 7, 2, 21, and 22 for the four locations, respectively. This study provides a theoretical basis for route planning of helicopter aerial spraying operations and a technical reference for smart forestry applications.

Analysing the penetration and droplet deposition characteristics in the canopy of fruit trees is critical for optimising the operational parameters of air-assisted spraying equipment, achieving precise application of chemicals, and improving the effectiveness of fruit tree pest and disease control. We used a mobile LIDAR system to detect the tree canopy characteristics and optical porosity and conduct wind tunnel experiments to investigate the interaction between the tree canopy, the airflow field, and the droplet penetration ratio in the canopy of fruit trees. The results show that the relative wind velocity decreases rapidly during canopy penetration, and that the minimum value occurs at the back of the canopy. The smaller the optical porosity, the greater the reduction in wind velocity is. The quadratic exponential regression model had the highest coefficient of determination (R2) (0.9672) and the lowest root mean square error (RMSE) (5.56%). This paper provides information on optimising the spraying parameters, improving the pesticide utilisation rate, and selecting the optimum spraying conditions and application parameters.

To reduce pesticide waste caused by low spray droplet coverage on target surface, the spraying system parameters of a six-rotor UAV sprayer are optimized. Computational Fluid Dynamics (CFD) is employed to simulate the downwash airflow distribution and droplet deposition for the UAV sprayer. Latin hypercube sampling method is used to obtain 15 sample points to train the surrogate model with CFD results. The validated CFD model is coupled with radial basis function neural networks (RBFNN) and genetic algorithm (GA) to optimize the spraying system parameters, including numbers of nozzles, horizontal position of nozzles and vertical position of nozzles. The overall correlation coefficient (R) of this RBFNN model is about 0.949, indicating good fitting performance. The optimal nozzles number of a six-rotor UAV sprayer is 4. The spraying droplet coverage is increased by 16.27%, 23.25% and 18.24% at a rotor height of 1 m, 2 m and 3 m above from the target surface, respectively, compared with the original six-rotor UAV spraying system structure. The spraying droplet coverage and pesticide utilization of the six-rotor UAV sprayer are improved through optimizing the mounting number and mounting positions of the nozzles in the spraying system. The research results provide a theoretical basis for further design and optimization of the multi-rotor UAV sprayers.

AbstractIt is still an open and challenging issue to the typical position control problems of the three‐axis electrical‐optical gyro‐stabilized platform systems (TEOGSP), due to inherent characteristics, for example, measurement noise, input saturation, parametric uncertainties, largely unknown load disturbance. To solve this problem, a saturated adaptive robust feedback controller using an adaptive cascaded extended state observer (SAFCESO) is proposed for compromising between the measurement noise effect and the sensitivity to disturbances. Firstly, the matched and mismatched disturbances existing in the TEOGSP system are estimated and rejected by the cascaded adaptive extended state observers (CESO). Secondly, the parametric uncertainties are evaluated by the adaptive control, and the match disturbances are attenuated by the robust control. Moreover, the adaptive robust control law does not require the velocity measurement signal and internal dynamics information of the system, which is practical to implement. Hence, all various uncertainties could be mainly compensated. Then, the improved auxiliary systems governed by smooth switching functions are developed and incorporated into the control design to compensate for the effect of the input saturation. Finally, the command filters are introduced to limit the magnitude of the virtual control and to calculate the derivative of the virtual control, respectively. The extensive comparative experimental results in the TEOGSP systems showed that the proposed SAFCESO method had superiorities in terms of high‐precision tracking accuracy, robustness, and noise reduction.

In this article, committed to extending the robust integral of the sign of the error (RISE) feedback control to the working condition of output feedback, a novel output feedback controller with a continuously bounded control input which combines the adaptive control and integral robust feedback will be proposed for trajectory tracking of a family of nonlinear systems subject to modeling uncertainties. A novel adaptive state observer (ASO) with disturbance rejection performance is creatively constructed to derive real‐time estimation of the unmeasured state signals. Moreover, a projection‐type adaption law is integrated to handle parameter uncertainties and an integral robust term is employed to deal with external disturbances. It is shown that asymptotic estimation performance and meanwhile asymptotic tracking result can eventually be derived. Simulation validations are implemented to demonstrate the high tracking performance of the presented controller. Notably, the synthesized control algorithm can be readily extended to the Euler–Lagrange systems. Typically, it can be extended to practical electromechanical equipment such as three‐dimensional vector forming robots to improve the real‐time forming accuracy.

In this article, the motion control problem of hydraulic lifting systems subject to parametric uncertainties, unmodeled disturbances, and a valve dead-zone is studied. To surmount the problem, an active disturbance rejection adaptive controller was developed for hydraulic lifting systems. Firstly, the dynamics, including both mechanical dynamics and hydraulic actuator dynamics with a valve dead-zone of the hydraulic lifting system, were modeled. Then, by adopting the system model and a backstepping technique, a composite parameter adaptation law and extended state disturbance observer were successfully combined, which were employed to dispose of the parametric uncertainties and unmodeled disturbances, respectively. This much decreased the learning burden of the extended state disturbance observer, and the high-gain feedback issue could be shunned. An ultimately bounded tracking performance can be assured with the developed control method based on the Lyapunov theory. A simulation example of a hydraulic lifting system was carried out to demonstrate the validity of the proposed controller.

In this article, a desired compensation version of the output feedback controller (DOFCESO) without using velocity measurement signal is proposed for precise tracking control of the three‐axis electrical‐optical gyro‐stabilized platform in the presence of largely unknown matched and mismatched modeling uncertainties. The proposed controller takes into account not only the system parametric deviations as well as the unmeasurable signal and external disturbances. To further handle the unmeasurable signal and the external disturbances, the composite extended state observer and nonlinear disturbance observer are constructed simultaneously via integrating adaptive nonlinear feedback tracking control design. To address the uncertainties arising from parametric deviations and external disturbances, the parameter adaptation mechanism is incorporated into the composite observer design to estimates of both the unmeasurable signal and the mismatched disturbance in the adaptive backstepping design. The tracking differentiator design is introduced here to estimate the derivative of the virtual control, leading to a much simpler control structure and reduced implementation costs. Furthermore, the proposed controller guarantees final tracking accuracy in the presence of time‐invariant modeling uncertainties and preserves the performance results of both control methods while overcoming their practical performance limitations. Extensive comparative experimental results are obtained to verify the high‐performance nature of the proposed control strategy.

Multi-rotor plant protection Unmanned Aerial Vehicles (UAVs) have suitable terrain adaptability and efficient ultra-low altitude spraying capacity, which is a significant development direction in efficient plant protection equipment. The interaction mechanisms of the wind field, droplet, and crop are unclear, and have become the bottleneck factor restricting the improvement of the deposition quality. This paper suggests a method to study the influence of the pesticide load on the detailed distribution law of downwash for a six-rotor UAV. Based on a hexahedral structured mesh, a 3D numerical calculation model was established. Analysis showed that the relative errors between the simulated and measured velocities in the z-axis were less than 11% when the downwash air flow was stable. Numerical simulations were carried out for downwash in hover under 0, 1, 2, 3, 4, and 5 kg loads. The effect of load on the airflow was evident, and the greater the load was, the higher the wind speed of downwash would be. Then, the influence of wing interference on the distribution of airflow would be more pronounced. Furthermore, under the rotation of the rotor and the extrusion of external atmospheric pressure, the “trumpet” phenomenon appeared in the downwash airflow area. As an extension, the phenomenon of the “shrinkage–expansion” was shown in the longitudinal section under heavy load, while the phenomenon of “shrinkage–expansion–shrinkage” was present under light load. After that, based on the detailed analysis of the downwash wind field, the spray height of this multi-rotor UAV was suggested to be 2.5 m or higher, and the nozzle was recommended to be mounted directly under the rotor and to have the same rotation direction as the rotor. The research in this paper lays a solid foundation for the proposal of the three-zone overlapping matching theory of wind field, droplet settlement, and canopy shaking.

Abstract In view of the poor thermodynamic environment problem of the self-powered launch of land-based concentric canister launcher (CCL), the launching scheme of injecting water at the bottom of launching tube is adopted to improve the thermodynamic environment of the launching system fundamentally. The solution program for liquid water vaporization is compiled and embedded into the homogeneous gas–liquid two-phase flow model, the source phase corrections of the momentum equation and the energy equation are also performed, and then the three-dimensional gas–liquid two-phase fluid dynamics model is established for the land-based CCL; analysis shows that the improvement of the thermal environment of the 35° and 45° water injection schemes is more better among these schemes. So coupling the mixture model, vaporization program and FW–H (Ffowcs Williams Hawkings) noise model, the noise distribution law in the bottom of the launcher cube for 35 and 45 water injection angles is discussed; in the intermediate frequency range, the −45° water injection scheme is about 2–10 dB higher than the noise signal of the −30° water injection scheme. Finally, it is recommended to optimize the overall thermal environment of the CCL by using the −30° preferred water injection scheme with both cooling effect and noise control.

针对路基同心筒自力发射过程载荷与热环境的评估问题,研究了两型路基中段导流同心筒的发射流场特性.以火箭发动机的纯气相燃气流场试验证实了本文采用的基于压力基耦合格式计算燃气流的可靠性;依托域动分层结构化动网格技术,对2种结构的中段导流同心筒自力发射三维流场进行了非定常数值仿真计算,分析了各同心筒方案发射过程流动机理及导弹载荷特性.三维计算表明:2种方案受"引射效应"和"倒吸效应"的影响都较小,导弹只在发射初期受到高温燃气流扰动;相同条件下,"内圆外方"方案的热环境、载荷特性稍逊于"内圆外圆"方案.

In this paper, an error-driven adaptive feedback control with an extended state observer (ANCESO) has been developed for the three-axis electrical-optical gyro-stabilized platform. The proposed controller takes into account not only the system parametric deviations as well as external disturbances via integrating adaptive nonlinear feedback tracking control and the extended state observer design. An improved error-driven nonlinear functions are constructed with feedback gain self-regulates to avoid the high gain chattering of the closed loop system. By integrating the fundamentally different working mechanisms of the approaches, the developed ANCESO strategy is able to preserve the theoretical performance results of both design approaches while overcoming their practical performance limitations. Comparative experimental results are obtained to validate the benefits and effectiveness of the proposed control strategy.

小型多旋翼植保无人机存在农药飘移、雾滴沉积不均等问题,为此本文结合RANS方程、SSTk-ω湍流模型和SIMPLE算法,对耦合六旋翼植保无人机下洗气流场和喷雾雾滴的两相流场进行数值模拟计算,探析环境风速对无人机下洗气流和农药雾滴沉积的影响.机身下方空间点风速试验和模拟值相对误差在15％以内,验证了数值模型的准确性.数值模拟结果表明:植保无人机飞行作业时,来流造成无人机下洗风场出现漩涡,来流速度对机身正下方流场的影响大于其对旋翼正下方流场的影响;侧风造成无人机下洗风场出现较大漩涡,下洗风场稳定性降低;两侧旋翼正下方对称布置喷嘴提高了雾滴沉积均匀性,来流造成雾滴卷积,雾滴飘移量随着来流速度提高而增大.综合各因素,无人机喷嘴应在旋翼下方对称布置,在小型六旋翼植保无人机实际作业时,无人机作业方向需要与外界环境风向保持平行,同时在晴朗环境下施药作业,从而降低雾滴飘移,提高农药利用率.

本文以农林类高校机械设计制造及其自动化专业为例,针对当前林业机械方向人才培养的专业性需求,结合"热工基础及流体力学"课程的特点,梳理目前课程教学中存在的典型性问题,浅谈林业机械方向"热工基础及流体力学"课程的教学构建.针对课程教学理论部分和后期核心专业课衔接脱节的问题,提出了适合农林类高校机械设计制造及其自动化专业本科生的教学思路;从内容调整、结构优化、自主学习等方面对课程进行了课程构建探讨;旨在加深学生对"热工基础及流体力学"课程的认知,充分调动学生对课程的学习兴趣,提高课堂教学质量和教学效果,强化学生的终身学习意识.

Committed to further enhancing the achievable tracking performance, a novel disturbance-compensation-based composite multilayer neural network adaptive control algorithm is developed for a class of multiple input multiple output nonlinear systems with modeling uncertainties. Specially, an extended state observer is utilized to estimate the exogenous disturbance and meanwhile predict the system state. Moreover, the nonlinear function uncertainties are approximated by the multilayer neural networks. Furthermore, the modeling uncertainties can be compensated in a feedforward manner. Notably, the multilayer neural network weights are updated via the composite adaption laws driven by the output tracking error and the prediction errors of the system state and control input, which brings improved function approximation performance. Finally, the application results demonstrate the efficacy of the integrated intelligent controller.