Research On Fault Diagnosis Method Of Motor And Bearing In Mine Ventilation System

In the process of coal mining,the mine ventilation system undertakes the ventilation and exhaust tasks of the entire mine,which plays an important role in the safe production of coal mines.Up to now,many coal mine gas explosion accidents have had direct or indirect relationship with ventilation system failures.In the coal mine ventilation system,the drive motor and the fan bearing are two key equipments and components.The operation status of the coal mine ventilation system directly affects or even determines whether the ventilation system can work normally.Especially with the development of power electronics technology,more and more fan drives have begun to use more efficient and energy-saving variable frequency power supply.In this case,the diagnostic accuracy of traditional spectrum analysis based fault diagnosis methods is reduced or even invalid.Therefore,more in-depth research on motor and bearing fault diagnosis methods is still of research value and significance.(1)Most of the mine ventilation system drive motors use cage asynchronous motors.In this paper,the motor rotor broken bar fault simulation model under power frequency is built.The characteristic frequency of rotor broken bar and air gap eccentric fault is analyzed from the electromagnetic point of view,and the time domain and frequency domain waveform of fault characteristic signal are given.The fan bearing fault mechanism is expounded.And the characteristics,and analyze the natural vibration frequency of the bearing and the frequency of local damage fault characteristics,which lays a foundation for subsequent research.(2)Due to the influence of various factors such as downhole environment change and weather during the operation of the fan system,the motor load changes frequently,and the diagnostic method based on spectrum analysis is obviously insufficient.The improved empirical mode decomposition is used to capture the fault characteristic frequency band,and the energy entropy is used to extract the fault characteristic information from the inconsistent energy distribution of different faults.The particle swarm optimization support vector machine algorithm is used to complete the motor rotor under power frequency conditions.Broken bar and air gap eccentric fault diagnosis,the classification effect is ideal,indicating the effectiveness of the algorithm.(3)The simulation model of rotor broken bar fault under asynchronous motor variable frequency speed regulation is built.The EEMD-energy entropy combined with PSO-SVM analysis method is used to diagnose the rotor broken bar fault and air gap eccentric fault when the motor is lightly loaded under the variable frequency speed regulation,and a good diagnostic effect is obtained.The method is verified under the variable frequency speed regulation.The fault diagnosis of the motor is superior to the traditional spectrum analysis method.(4)Apply the above feature extraction and pattern recognition methods to fan bearing fault diagnosis.Combined with the measured data,the normal operation of the bearing under the power frequency condition,the slight fault of the inner ring,the severe fault of the inner ring,the mild fault of the outer ring,the severe fault of the outer ring,the mild fault of the rolling element and the rolling weight fault are realized.Classification identification,better diagnostic results,verify the effectiveness of the algorithm and the applicability of bearing fault diagnosis,and discuss the adverse effects of the use of the frequency converter on bearing fault identification from the aspects of harmonics,harmonic torque and carrier frequency.