Design And Research Of Online Monitoring And Fault Diagnosis System For Mine Main Fan

As the core equipment of the mine ventilation system,the mine main fan is responsible for providing fresh air to the underground workers,reducing the concentration of harmful gases and ensuring a good working environment.The failure and shutdown of mine main fan will cause great threat to the coal mine,and even threaten the life safety of workers.Therefore,the research on online monitoring and fault diagnosis system of mine main fan has important practical application value.In this paper,the mine main fan as the research object,according to the structural characteristics of the main fan,the common faults are studied and analyzed,and the relationship between the fan fault type and vibration signal is established.And the common fault diagnosis technology is summarized and introduced.The control cabinet with PLC as the core controller is designed to realize the frequency conversion control of fan,the start and stop control of air door winch,the acquisition of vibration temperature,air volume and pressure signal,and the monitoring of motor operation parameters.According to the above purpose,the PLC control program is compiled and the upper computer configuration interface is designed.Using the intelligent gateway,the online monitoring function of PLC,cloud server and remote device is realized.The fault diagnosis algorithm is designed,and the noise reduction effect of four singular value screening methods of SVD noise reduction technology is compared.It is found that the singular value differential spectrum peak selection method has the best noise reduction effect.The average method and simple method in data reconstruction are compared and analyzed.It is found that the effect of simple method is similar to that of average method,but the amount of calculation is smaller.The wind turbine fault diagnosis test-bed is developed.Six kinds of wind turbine faults are simulated.The SVD noise reduction technology is used to reduce the noise of the vibration data collected by the monitoring system.The singular value screening and data reconstruction are studied.The singular value differential spectrum peak selection method and simple method are selected to reconstruct the data.The empirical mode decomposition with white noise is adopted.By comparing the seven evaluation indexes,it is found that the normalized energy variance value,the maximum spectrum value and the normalized signal power reciprocal value are the most suitable characteristic signals for fault signals.The eigenvalue of the decomposed modal function is solved and the eigenvector table is constructed.The neural network algorithm is used to classify the faults of the fan.The neural network is trained and tested by the eigenvector table.Through data interaction,the data is interconnected with the fault diagnosis interface in the upper computer interface.In this paper,through the in-depth analysis of the theoretical knowledge of the mine main fan online monitoring and fault diagnosis system,based on the completion of the mine main fan monitoring,the effective fault diagnosis of the fan is realized.It achieves the requirements of stable information transmission,fast response and high fault identification rate,improves the safety of coal mine production,and has certain theoretical research and engineering application value.There are 91 pictures,17 tables and 101 references in this paper.