Research And Implementation Of The Elevator Fault Prognosis And Remote Monitoring Platform

With the rapid development of the modern city, more and more high-rise residence and high-end office space are brought up, the elevator has become an essential vertical transport method of people. How to find faults in time, ensure the reliable operation of the elevator and maintainent intelligent elevators to protect people’s security has become a very important research topic in the development of modern society.Firstly, this thesis introduces the background of the elevator monitoring, summarizes the development status of the domestic and international elevator remote monitoring system, and researches the needs of the potential users of the elevator monitoring system. Secondly this thesis introduces the elevator fault detection and diagnosis methods. After the thesis analysis the diagnosis based on the analytical model, signal processing model and knowledge-based model analyzes and compares methods to select the best model for elevator fault diagnosis. In elevator fault prognosis part, the thesis analyzes the research status of the domestic failure prognosis and uses time series forecasting model and neural network prediction to analyze. After that this thesis uses time series and neural network to do fault prognosis. The result proves that the based on neural network series forecasting method is feasible. Taking into account the elevator malfunction actual situation, this thesis optimizes the Holt-Winters time series model which is proposed to be more suitable for the prediction of the elevator failure. According to the prediction results, this thesis works out an elevator maintenance plan.Finally, this thesis proposes the overall framework of the elevator remote monitoring platform based on Internet of Things, and designs the database. This thesis implements the elevator distribution statistics module, elevator real-time monitoring module, elevator failure analysis module, elevator malfunction module and platform information management module. Then this thesis summarizes conclusions and future work.