Fault Diagnosis And Research Of A Blast Furnace Charging Transmission Gearbox Bearing

Bearing plays an important role in industrial production.If the bearing failure will have serious consequences,and even lead to a major accident equipment.Before the occurrence of major accidents,timely make the initial prediction,reduce economic losses,is the focus of fault detection.In this paper,the bearing of the gear box of the charging drive system of a blast furnace in an iron smelting plant is tested and studied.The vibration signal of the tested gear box bearing is collected and its characteristic frequency is calculated.Based on the time domain and frequency domain analysis of vibration diagnosis technology of mechanical equipment,the fault of gearbox bearing is analyzed and inferred.According to the information displayed in the time domain and frequency domain waveform,the information processing and fault analysis are carried out.The fault location is confirmed by the impact of time domain signal law and the wave crest in frequency domain,and the characteristic frequency of different components is different.At the same time,BP neural network analysis method is used to verify and confirm the fault of gearbox bearing.The accurate levenberg-marquardt algorithm based on BP neural network is used to verify bearing faults.The suspicious fault was studied and trained with matlab software,and then the field measured data was imported for testing.The final fault diagnosis conclusion is consistent with the actual situation on site,and after the replacement of the corresponding parts,everything runs normally.By analyzing the measured vibration signal of gearbox bearing and analyzing the neural network,various faults can be effectively identified to help enterprises solve the early failure of mechanical equipment,improve the fault diagnosis efficiency and reduce the maintenance cost of equipment.