Health State Assessment And Performance Degradation Prediction Of Electromechanical Equipment Based On Multi-source Information Fusion

Electromechanical equipment in the development of human has been played an important role in the process.The emergence of electromechanical equipment has liberated the human labor.With the development of the society,it also ushered in the rapid development,and towards complicated,precision,automation,intelligent direction.People have been working in the environment of these large and electromechanical equipment,once an accident occurs,it will cause disastrous consequences for personal safety and social economy.Therefore,performing operations such as health state evaluation and performance degradation prediction of electromechanical equipment and understanding its health status during operation is of great significance to ensure safe production.With the support of the National Natural Science Foundation of China and the Hainan Provincial Key Research and Development Program.,this paper mainly discusses and explores the problems in health state assessment and performance degradation prediction of electromechanical equipment.Propose the method based on semi-quantitative information to solve the problems existing in the existing methods.On the one hand,the speed and degree of performance degradation of electromechanical equipment at different stages in the full life cycle are quite different.When the performance degradation indicators evaluate the health of the whole life of machine,the degree of influence at different stages is different.For example,there is no trace of degradation in the early stage,but the degradation rate in the later stage is increased.Therefore,when establishing the performance degradation curve,the importance of performance indicators in different stages of the life cycle should be treated differently,with special attention to the later stage of degradation.The variable working conditions of complex machinery and equipment should be considered mainly.Complex machinery and equipment are often in an environment where multiple working conditions are switched at any time during operation,and different working conditions have very different effects on the degradation trend of complex machinery and equipment.Continuous changes in conditions will also have a great impact on the degradation rate of electromechanical equipment.However,most of the current studies have not considered the impact of multi-condition switching on the life.These issues should be focused on in the research process.To solve the above problems,a novel method of Adaptive Evidence Reasoning(AER)is proposed,which assigns different weights to different degradation periods and different types of monitoring data to distinguish the importance of different performance degradation stages.Therefore,it is necessary to study The entire life cycle of the object is modeled in stages Introduce the performance degradation evaluation index for the whole life cycle of electromechanical equipment,and use it as the objective function to obtain the optimal weight parameter value corresponding to each stage through algorithm optimization,and finally obtain the performance degradation curve of electromechanical equipment by the evidence reasoning rule algorithms.On the other hand,in the process of assessing the health status of electromechanical equipment,most studies do not effectively use the multi-source information collected by multiple sensors,and rely too much on quantitative data and ignore the qualitative knowledge of experts,which makes it impossible to effectively use qualitative and quantitative information when establishing performance degradation curves,and lack of unity in the modeling process A framework for expressing various uncertainties.Regarding the issue above,a performance degradation prediction method based on the Belief Rule Base is proposed.The BRB-based performance degradation prediction model of the electromechanical system is constructed by fusing multiple monitoring information and using the historical value of health evaluation.This method can describe various types of uncertain information in multi-attribute decision-making problems by establishing a unified confidence frame,and effectively utilize qualitative knowledge and quantitative information.