Fault Diagnosis And Application Of Playground Mechanical Equipment Based On Improved SVM

In recent years,due to amusement park recreation equipment,mechanical problems to the amusement park amateurs bring the loss of life and property are not uncommon.Therefore,it is very important to study an efficient and convenient method for fault diagnosis of mechanical equipment in playground.Increasingly large-scale,complex,automated,integrated playground machinery and equipment makes the traditional method of fault diagnosis has been unable to meet the current complexity of the playground machinery and equipment fault diagnosis.Aiming at the above problems,this paper proposes an improved support vector machine mechanical fault diagnosis model,and applies it to the amusement park roller coaster mechanical equipment fault diagnosis.Based on the background of fault diagnosis of roller coaster,the paper introduces the structure model of roller coaster,the failure mode of roller coaster,the mechanism of roller coaster vibration,the signal characteristics of roller coaster,the feature extraction of chaotic sequence signal,simulated annealing improved support vector machine,gravitation algorithm support vector machine and the roller coaster mechanical failure diagnosis plan research.The specific research contents are as follows.(1)To build a roller coaster structure simplified modelThis paper analyzes the structural components of the roller coaster mechanical equipment system,analyzes the components of the roller coaster by means of mechanics analysis,describes the failure theory of the roller coaster,establishes a simplified model of the fault diagnosis of the roller coaster dangerous parts,and the feasibility of the simplified model is analyzed from the point of mechanical and mechanical failure.(2)Analysis of roller coaster failure mode and vibration mechanismThe mathematic model of the system vibration of roller coaster is established,which will lay a theoretical foundation for the following signal analysis.The finite element analysis shows that the dangerous parts of the roller coaster are the bearing,the main failure modes are bearing inner ring failure,outer ring failure and rolling element failure.(3)Roller coaster fault signal characteristic analysis and fault feature extractionAccording to the vibration mechanism of roller coaster,it is divided into inner ring fault,outer ring fault and rolling element fault signal analysis,which is based on the vibration mechanism of the rolling bearing group of roller bearing faulty parts.The relationship between the fault signal and the fault feature is established.In view of feature extraction is between 0 ~ 1 search optimization problem,and chaos search is suitable for solving feature extraction problem.Therefore,chaos search is chosen as the tool of feature extraction for roller coaster fault signal.(4)Improved support vector machine model establishment and solutionFirstly,based on the global searching characteristics of the simulated annealing algorithm,the optimal parameters of the support vector machine and the width of the kernel function are optimized.The improved support vector machine model is obtained and used to diagnose the mechanical failure of the roller coaster.MATLAB software solution shows that the classification accuracy of the model as high as 98.39%.Secondly,based on the characteristics of the gravitational algorithm with high searching speed,this method is used to optimize the support vector machine and the kernel function.The support vector machine model is improved and used in the fault diagnosis of roller coaster.The result shows that the classification accuracy of the model is as high as 98.66%.Combined with signal feature extraction method and improved support vector machine,chaotic universal gravitational support vector machine(CGSA-SVM)and chaotic simulated annealing support vector machine model(CSAA-SVM)are established respectively.The two model is applied to the fault diagnosis of playground roller coaster.The results show that the correctness of the two models is 98.78% for the fault diagnosis of the roller coaster.This indicates that the chaotic simulated annealing support vector machine model and the chaotic universal gravitation support vector machine model are reliable for the mechanical fault diagnosis of the playground roller coaster.