The Technology And Application Of Emergency Stop Failure Diagnosis Based On Operating Characteristic Big Data Analytics For High-Speed Elevator

As the indispensable vertical transport in modern high-rise buildings,high-speed elevator must meet the quality requirements in functional and safety.High-speed elevator has has the characteristics of fast in running speed,high height of lifting and acceleration variable.There will be severe vibration and obvious noise in the event of emergency stop fault,which affects comfort and safety seriously.High-speed elevator is often based on the network of the digital operation monitoring technology.There will be big data in the operation of high-speed elevator,which contains connection between the operation data and the fault.Therefore,it is very important to dig out the fault information from the operation data and optimize the fault diagnosis method for improving the security and stability of high-speed elevator.On the basis of reviewing the method of fault diagnosis for mechanical equipment,study deeply for the cause of the emergency stop of high-speed elevator,the mapping relation between the cause and the large data of running characteristic,the data extraction and diagnosis of the emergency stop feature.Develope a feature data analysis and diagnosis system for emergency stop fault of high-speed elevator.And the system was successfully applied in emergency stop fault diagnosis of high-speed elevator.The thesis organized as follows:The first chapter reviewed the current research of big data analysis and fault diagnosis,fault diagnosis method of electromechanical equipment,elevator fault diagnosis method at home and abroad.Moreover,the background of research,main contents and the framework of this thesis is introduced.The second chapter based on analyzing the emergency stop causes and the operation characteristics data of high-speed elevator,collected the running characteristic parameter data of high-speed elevator.Established a hierarchical structure model for the cause of emergency stop.Established the relationship between the characteristic parameters and the cause of emergency stop fault based on the hierarchical mapping.The third chapter apply dyadic wavelet transform which has the characteristics of high resolution in time-frequency domain.Proposed an algorithm of emergency stop fault feature denoising for high-speed elevator based on wavelet modules maximum.Apply rough set attribute reduction to extract the characteristic parameters of the emergency stop for high-speed elevator.The fourth chapter study the fuzzy BP neural network for the emergency stop fault diagnosis of high-speed elevator,the fault diagnosis method for the emergency stop of the high-speed elevator based on the neural network,the diagnostic process for the emergency stop of the high-speed elevator based on the neural network.Established the fault diagnosis model of the emergency stop for high-speed elevator.The big data analysis and emergency stop fault diagnosis of high-speed elevator have been achieved.The fifth chapter developed a big data analysis and diagnosis system for emergency stop fault of high-speed elevator on the basis of the theory and methods discussed above.The system was successfully applied in the emergency stop fault of high-speed elevator.The sixth chapter summarizes the main research work and innovations of this thesis.Put forward the further researeh according to the current deficiencies.