The Research Of Prediction On The Residual Life Of Mining Equipment Management Technology

With the rapid development of science and technology, the structure in modern society is increasingly complicated,and the safety and reliability of equipment has aroused widespread concern, at the same time,the research based on analyzing and forecasting remaining life has greatly developed at home and abroad in recent years.The principal purpose of monitoring the machine condition is to analyze and forecast the remaining life about device.The main technology will be applied is trend analysis,which analyzes obtained datum timely and determines the equipment's status and development trend in the future though measuring equipment regularly. Finally, it realizes predictive maintenance goal.It engages more and more people's attention because of its significant practical value.Since artificial neural network is used for the first time, neural network has played an important role in the field of fault diagnosis and evaluation, because of its real-time operation,anti-jamming capability and high rate of accuracy.Because the mine hoist is so significant in mine that it is necessary to predict the life of mine hoist.Firstly, BP neural network is wildly discussed in the study, and it is also studied in this article, discussing and comparing several improved BP neural network. Secondly, introducing the mine hoist features and work conditions,the core is to establish the system model,which is based on BP neural network, Borland C++ Builder and Matlab.Finally, the system is used to predict lifetime of mine elevator shaft, the results show that this system meet the requirements.