Research On Condition Monitoring And Evaluation Method Of Airline Baggage Consignment Equipment

From manual operations to mechanization,automation,airport operations in various countries around the world are continuously upgraded and iterated to the intelligent era.Improving the efficiency and experience of aviation travel has become an important requirement for the construction of smart airports.The self-service air baggage check-in equipment is a key equipment for smart airport construction.In order to ensure the smooth progress of baggage check-in business,reduce equipment maintenance costs and improve service quality,it has become an inevitable need to carry out research on condition monitoring and evaluation methods of self-service baggage check-in equipment.This subject mainly focuses on the research on the health status and operational capability assessment methods of airport self-service baggage check-in equipment.By collecting the data of the self-service baggage check-in system’s remote monitoring platform and local server,the self-assessment module of the equipment operating status is developed to realize the efficient operation and management of the equipment.First of all,in view of the problem that the current self-service baggage check-in equipment only considers a single type of data for state evaluation,and the data utilization rate and decision-making credibility are low,a method of equipment state evaluation that integrates the proportional hazard regression model and the Wiener process is proposed.Combining the equipment business log and status monitoring information,the data is divided into event type data and status type data.The risk covariates are extracted from the event-type data,and the equipment state mutation model based on the proportional hazard regression model is constructed to obtain the probability of sudden failure of the equipment under the influence of multiple risk factors.At the same time,state-type data is used to define composite degradation indicators to characterize the amount of equipment degradation.Build a gradual model of equipment state based on the Wiener process,to obtain the comprehensive health state value of the equipment.Qualitative analyze the overall state of the equipment,and use actual equipment operating data to verify the engineering effectiveness of this method.Secondly,aiming at the problems of unclear equipment operation capability and low management efficiency,a GA-BP-based equipment operation capability evaluation method is proposed.By analyzing a large amount of business transaction data recorded by the self-service baggage check-in system,the equipment failure mode and influence system is established,the abnormal rate of key sub-equipment is selected as the input parameter of the model,and the BP neural network model optimized by genetic algorithm is established.Use the equipment historical monitoring data to train the model to obtain the optimal network weights and thresholds.Then use this model to predict the equipment operation capability in real time,and reduce the cost of equipment operation and maintenance.Experiments show that this method has a better predictive effect.Finally,the general data set and the real operating data of the self-service baggage check-in equipment were used for experimental analysis and verification,and a visual interface was designed based on this,and the design and development of the entire system was finally completed.