Research on Nonlinear Control Method of Underactuated Gantry Crane Based on Machine Vision Positioning

The movement of the gantry crane is controlled by an symmetry underactuated system, and has poor robustness in precise positioning. A new active control method based on the machine vision positioning is proposed in this paper, and the trajectories are planned after the detection of starting and ending points. A new type of energy storage function is given in this paper, and a coupling control law is derived to minimize the load swing in the process of precise positioning. The equilibrium point of the closed-loop system is checked though Lyapunov and LaSalle’s theorems, and the calculation results are verified through experimental investigations. The results show that the equilibrium points are asymptotically stable, and the proposed control method is of better robustness. The findings provide a new kind of control method with higher efficiency, and can help with the precise control of gantry cranes

Research on Nonlinear Coupling Anti-Swing Control Method of Double Pendulum Gantry Crane Based on Improved Energy

The double pendulum type gantry crane is a typical symmetry underactuated motion system. It has control problems in that the swing of the payload is difficult to suppress and the precise positioning of the trolley is not accurate. A new nonlinear coupling control method based on improved energy is proposed in this paper. We define coupled control signal among trolley, hook and payload. An improved energy storage function is established based on the new coupling control signal. Consequently, a nonlinear anti-swing controller is constructed straightforwardly, and the closed-loop system stability is subject to strict mathematics analysis by Lyapunov and LaSalle’ s theorem. Moreover, the new energy function based on the coupling behaving between the trolley motion and the payload swing leads to the improved control performance. Numerical simulation results show that the proposed method has better performance than traditional controllers. It not only effectively suppresses the swing of the load and the hook, but also precisely controls the displacement of the trolley. It has strong robustness to the displacement of the payload, the change of the gantry crane parameters and the external disturbance

Trajectory Prediction of Assembly Alignment of Columnar Precast Concrete Members with Deep Learning

During the construction of prefabricated building, there are some problems such as a time consuming, low-level of automation when precast concrete members are assembled and positioned. This paper presents vision-based intelligent assembly alignment guiding technology for columnar precast concrete members. We study the video images of assembly alignment of the hole at the bottom of the precast concrete members and the rebar on the ground. Our goal is to predict the trajectory of the moving target in a future moment and the movement direction at each position during the alignment process by assembly image sequences. However, trajectory prediction is still subject to the following challenges: (1) the effect of external environment (illumination) on image quality; (2) small target detection in complex backgrounds; (3) low accuracy of trajectory prediction results based on the visual context model. In this paper, we use mask and adaptive histogram equalization to improve the quality of the image and improved method to detect the targets. In addition, aiming at the low position precision of trajectory prediction based on the context model, we propose the end point position-matching equation according to the principle of end point pixel matching of the moving target and fixed target, as the constraint term of the loss function to improve the prediction accuracy of the network. In order to evaluate comprehensively the performance of the proposed method on the trajectory prediction in the assembly alignment task, we construct the image dataset, use Hausdorff distance as the evaluation index, and compare with existing prediction methods. The experimental results show that, this framework is better than the existing methods in accuracy and robustness at the prediction of assembly alignment motion trajectory of columnar precast concrete members

Research on the Initial Fault Prediction Method of Rolling Bearings Based on DCAE-TCN Transfer Learning

In actual working conditions, the initial faults of rolling bearings are difficult to effectively predict due to the lack of evolution knowledge, weak fault information, and strong noise interference. In this paper, a rolling bearing initial fault prediction model that is based on transfer learning and the DCAE-TCN is presented. Firstly, a deep autoencoder (DAE as the first two hidden layers and CAE as the last hidden layer) is used to extract fault features from the rolling bearing vibration signal data. Then, the balanced distributed adaptation (BDA) is used to minimise the distribution difference and class spacing between extracted fault features, and a common feature set is constructed. The temporal features of the original vibration signal in the target domain are extracted using the advantages of the TCN. The experiments are conducted on the publicly available XJTU-SY dataset. The experimental results show that the proposed method can effectively learn the transferable features and compensate the differences between the source and target domains and has a promising application with higher accuracy and robustness for the prediction of early failures of rolling bearings

An R

An R2 indicator based selection method is a major ingredient in the formulation of indicator based evolutionary multiobjective optimization algorithms. The existing classical indicator based selection methodologies have demonstrated an excellent performance to solve low-dimensional optimization problems. However, the R2 indicator based evolutionary multiobjective optimization algorithms encounter enormous challenges in high-dimensional objective space. Our main purpose is to explore how to extend the R2 indicator to handle many-objective optimization problems. After analyzing the R2 indicator, the objective space partition strategy, and the decomposition method, we propose a steady-state evolutionary algorithm based on the R2 indicator and the decomposition method, named, R2-MOEA/D, to obtain well-converged and well-distributed Pareto front. The main contribution of this paper contains two aspects. (1) The convergence and diversity for the R2 indicator based selection are analyzed. Two improper selection situations will be properly solved via applying the decomposition method. (2) According to the position of a new individual in the steady-state evolutionary algorithm, two different objective space partition strategies and the corresponding selection methods are proposed. Extensive experiments are conducted on a variety of benchmark test problems, and the experimental results demonstrate that the proposed algorithm has competitive performance in comparison with several tailored algorithms for many-objective optimization

Theoretical and Experimental Analysis of Thin-Walled Curved Rectangular Box Beam under In-Plane Bending

Thin-walled curved box beam structures especially rectangular members are widely used in mechanical and architectural structures and other engineering fields because of their high strength-to-weight ratios. In this paper, we present experimental and theoretical analysis methods for the static analysis of thin-walled curved rectangular-box beams under in-plane bending based on 11 feature deformation modes. As to the numerical investigations, we explored the convergence and accuracy analysis by normal finite element analysis, higher-order assumed strain plane element, deep collocation method element, and inverse finite element method, respectively. The out-of-plane and in-plane characteristic deformation vector modes derived by the theoretical formula are superimposed by transforming the axial, tangential, and the normal deformation values into scalar tensile and compression amounts. A one-dimensional deformation experimental test theory is first proposed, formulating the specific contributions of various deformation modes. In this way, the magnitude and trend of the influence of each low-order deformation mode on the distortion and warping in the actual deformation are determined, and the significance of distortion and warping in the actual curved beams subjected to the in-plane loads is verified. This study strengthens the deformation theory of rectangular box-type thin-walled curved beams under in-plane bending, thus providing a reference for analyzing the mechanical properties of curved-beam structures

RESEARCH ON LOCATION OF SPALLING FAULT LOCATION OF BALL BEARING OUTER RING BASED ON THE HORIZONTAL VERTICAL SYNCHRONIZATION PEAK RATIO (MT)

The outer ring spalling failure angle position of the rolling bearing has an important influence on the vibration size and service life of the rotating machine. It is necessary to judge the outer ring spalling failure angle within the load-bearing interval. Based on the peak change law of the acceleration vibration signal, a fault location method was proposed for ball bearing outer ring spalling. This method established the Lagrangian dynamic equation with time-varying impact force. By analyzing the change trend of the contact force in the bearing area of the ball bearing, the law of the peak ratio of the absolute value of the vibration acceleration signal was obtained by analogy, and the impact point of the acceleration vibration signal was analyzed. For nearby locations, a new judgment index was proposed, and the specific location of the fault was obtained. Simulation and experiment results show that there is a corresponding functional relationship between the horizontal vertical synchronization peak ratio of the vibration signals at different positions of the ball bearing outer ring spalling fault. Simulation and experiment verify the accuracy of the positioning law. It provides a theoretical basis for the diagnosis method to determine the location of the ball bearing outer ring spalling fault location

Improved Parallax Image Stitching Algorithm Based on Feature Block

Image stitching aims at generating high-quality panoramas with the lowest computational cost. In this paper, we present an improved parallax image-stitching algorithm using feature blocks (PIFB), which achieves a more accurate alignment and faster calculation speed. First, each image is divided into feature blocks using an improved fuzzy C-Means (FCM) algorithm, and the characteristic descriptor of each feature block is extracted using scale invariant feature transform (SIFT). The feature matching block of the reference image and the target image are matched and then determined, and the image is pre-registered using the homography calculated by the feature points in the feature block. Finally, the overlapping area is optimized to avoid ghosting and shape distortion. The improved algorithm considering pre-blocking and block stitching effectively reduced the iterative process of feature point matching and homography calculation. More importantly, the problem that the calculated homography matrix was not global has been solved. Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image. The performance of the proposed approach is demonstrated using several challenging cases

Hybrid Prediction Model of the Temperature Field of a Motorized Spindle

The thermal characteristics of a motorized spindle are the main determinants of its performance, and influence the machining accuracy of computer numerical control machine tools. It is important to accurately predict the thermal field of a motorized spindle during its operation to improve its thermal characteristics. This paper proposes a model to predict the temperature field of a high-speed and high-precision motorized spindle under different working conditions using a finite element model and test data. The finite element model considers the influence of the parameters of the cooling system and the lubrication system, and that of environmental conditions on the coefficient of heat transfer based on test data for the surface temperature of the motorized spindle. A genetic algorithm is used to optimize the coefficient of heat transfer of the spindle, and its temperature field is predicted using a three-dimensional model that employs this optimal coefficient. A prediction model of the 170MD30 temperature field of the motorized spindle is created and simulation data for the temperature field are compared with the test data. The results show that when the speed of the spindle is 10,000 rpm, the relative mean prediction error is 1.5%, and when its speed is 15,000 rpm, the prediction error is 3.6%. Therefore, the proposed prediction model can predict the temperature field of the motorized spindle with high accuracy

Effect of Thermal-Related Fit Clearance between Outer Ring and Pedestal on the Vibration of Full Ceramic Ball Bearing

Full ceramic bearing can work under a wide range of temperatures, but the thermal deformation difference between the ceramic outer ring and steel pedestal has a great increase with the rise of temperature and leads to obvious impact and friction. In this paper, the thermal deformation difference is considered and the fit clearance is taken as the boundary condition of the dynamic model. Investigations on the dynamic response of the outer ring are conducted, and the effect of thermal-related fit clearance is analyzed at different working temperatures and rotation speeds through parametric study and experiments. Results show that the vibration of the outer ring grows with the temperature and shows different changes with rotation speed as the temperature changes. The variation of working temperature brings difference in the interactions between the outer ring and the pedestal, and the trends of vibration with rotation speed also change at different temperatures. Impact and friction make great contributions to the interactions between the outer ring and the pedestal and show different changes with temperature. This study puts forward a method for the calculation of bearing vibration at variable temperatures and provides theoretical basis for the application of the full ceramic bearings