Ventilator' Condition Monitoring And Diagnosis System Based On Internet Plus

As an important large-scale equipment of mining enterprises,ventilator is responsible for conveying a large amount of fresh air to the mine,ensuring the safety of underground working environment and maintaining the stable operation of production process.The safe and stable operation of ventilators is related to the production efficiency of mining enterprises and the life safety of underground workers.Therefore,it is necessary to monitor and diagnose the ventilators online.The application of Internet technology and data sharing to on-line remote monitoring and intelligent diagnosis of ventilators is a necessary means to achieve effective and accurate monitoring and fault diagnosis.Based on the analysis of the development of on-line monitoring and fault diagnosis technology and the consideration of the actual situation of on-line monitoring of mine ventilators in China,an on-line monitoring and fault diagnosis system for mine ventilators is established.By using network and intelligent technology,the fan monitoring data can be processed and managed effectively.By sharing the corresponding monitoring data,the utilization rate of data can be improved,the database of fault diagnosis can be enriched,and the accuracy of monitoring and diagnosis can be improved.In this paper,according to the actual operation of mine ventilators,through the selection of various sensors and intelligent instruments,the lower computer uses PLC technology to collect the monitoring data of the fan operation status,and realizes the online collection of the status data.The upper computer uses Kingview software to configure the various ventilator stations,realizing the functions of real-time display,alarm and recording of monitoring data.Aiming at the characteristics of many fan sites and scattered locations,based on the construction of TCP/IP-based Internet structure,a database management software platform based on SQL Server is developed,which realizes effective storage and visual query and management of a large number of monitoring data.On the basis of using wavelet packet decomposition and energy feature extraction technology to obtain eigenvalues of vibration data,a BP neural network system based on thought evolutionary algorithm is constructed to realize the fault diagnosis of the key component of ventilator-bearing.According to the characteristicsof many monitoring parameters and huge amount of data,eigenvalues are extracted from various monitoring data,and eigenvalues are intersected.Fork processing,LLE dimension reduction method is used to reduce the dimension of features,and neural network model is used to realize the prediction of fan operation status.Based on Internet technology,the exchange and data sharing of monitoring data and diagnostic prediction model parameters at different monitoring sites are realized by using WebService,intranet penetration technology and data sharing client developed on VS platform.