Research On Typical Elevator Fault Monitoring And Early Warning System Based On Internet Of Things

With the rapid increase of the number of elevators,the relationship between elevators and production and daily life is getting closer more and more close.How to ensure elevators operating steady and the faults be diagnosed and warned timely has become the focus of the government and relevant scholars.At the same time,the rise of the Internet of things technology has brought advanced intelligent sensing technology,so the elevator operation data that can be collected not only has been greatly improved in quantity and real-time,but also has more and more kinds.There is a huge amount of knowledge hidden behind these data,which provides more opportunities for the development of elevator fault monitoring and early warning technology.Under this background,this paper carries out monitoring and early warning for the corresponding faults in the main components of traction elevator,control system and mechanical system,and displays the monitoring and early warning results visually.The main contents of system development are as follows:(1)Based on the requirement analysis of elevator typical fault monitoring and early warning system,the overall architecture of the system is designed,including data acquisition and transmission module,data distribution module,data and business logic processing module and result visualization module.At the same time,the overall workflow of the system is introduced.(2)Aiming at the design of elevator operation data acquisition and transmission system,this paper chooses the corresponding Internet of Things transmission technology by comparing the technical performance of Internet of Things transmission and combining the characteristics of elevator operation data and acquisition environment.On this basis,a complete elevator operation data acquisition and transmission system is designed to store the data collected from sensors into the database of local server cluster.(3)Aiming at solving the elevator control system fault,a fault monitoring method of elevator control system based on finite state machine is proposed.Firstly,the finite state machine(FSM)is used to model the operation state transition process of the elevator control system.Secondly,the performance of the data flow processing framework is compared and selected.Then,the Flink computing framework with better performance is used to collect FSM to process and logically judge the elevator operation data.When the illegal state transition is detected and the corresponding fault type is judged,the diagnosis and the fault data is stored in the database for further display and management.(4)Aiming at solving the elevator car system fault,a fault early warning method of elevator car system based on DAE-t SNE algorithm is proposed.Through online model training and online fault early warning,the real-time early warning of the car system fault and health status is carried out,and the results are visualized.The model can help the elevator managers master the health status of the elevator car system in real time,and carry out preventive maintenance for the degraded car system.(5)The typical elevator fault monitoring and early warning system based on Internet of things is designed and developed in detail.The system integrates the functions of elevator fault monitoring and early warning,and can display the results visually.