Research On Servo Motor Performance Degradation Assessment And Prediction

Mechanical equipment is widely used in modern production and life and is developing towards high reliability,heavy load and high efficiency.System failures reduce production efficiency and even expose personal safety to dangers and cause safety problems.The classical fault diagnosis technology evaluates the operating state of the system as much as possible.The purpose of the performance degradation assessment is to define and discriminate the system state in stages.The performance degradation prediction seeks to give a general trend of the remaining life of the system..Degradation assessment and residual life prediction of equipment can help to master the initiative of equipment maintenance.This kind of predictive research can effectively reduce the incidence of equipment accidents and reduce unexplained downtime maintenance.It has important realities in engineering practice.significance.The reliability of the motor directly affects the safety and economic benefits of production.The evaluation and prediction of fault diagnosis and performance degradation has always been the focus of attention in related fields.However,the research based on its life test has considerable blank.In this paper,based on the continuous hidden Markov model and the autoregressive model,the bearing is used as the algorithm verification,and the servo motor test is used as the experimental verification.The performance degradation assessment and life prediction based on the lifetime data are carried out.The main research core content includes:Combined with the large framework of state maintenance and its theoretical basis,the research background of the topic is described with practical requirements as the starting point,and the relationship between fault diagnosis and performance degradation assessment prediction is described.The current situation and application prospects in the field of performance degradation assessment and prediction research at home and abroad are analyzed,and the technical route and article framework are determined.The concept composition and basic algorithm of hidden Markov model are introduced.The evaluation problems,decoding problems and learning problems of hidden Markov model are discussed in depth.A continuous hidden Markov model suitable for processing continuous signals is introduced for performance evaluation,and the performance of different types of bearings is evaluated by the log likelihood ratio of its output.The performance degradation results are finally obtained to show the deviation of the equipment.The extent of normal operating conditions and a phased description.Reviewing the life prediction method,using the autoregressive model to predict the performance degradation based on the evaluation results,and comparing and discussing the method based on the trend extrapolation method,the prediction results give the approximate life trend of the bearing.Grasping the remaining life of the equipment has certain reference significance.Investigate the servo motor and life test,build a servo motor full life test bench for full life test and record the life data,test flow and test bench operation status.Based on the fault analysis,the continuous hidden Hidden Markov model is used to evaluate the performance degradation of servo motor.The autoregressive method model is used and the trend extrapolation method is used as a comparison to predict the performance degradation.The purpose of the assessment and prediction of performance degradation is to observe the degree of degradation of the system to some extent and give equipment operation guidance for real production.The phased performance degradation assessment helps to grasp the actual operating state of the equipment,while the performance degradation prediction gives a way to grasp the remaining operational time of the equipment based on the trend of equipment life.