Comparison of beta-kurtosis and kurtosis methods for troubleshooting the performance of a transmission vehicle using vibrating frequencies

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    One of the main methods in maintenance and repair is a preventive maintenance method that is often more effective. The requirements of this method are to monitor the performance of machinery during operation. One of the car's functions that is monitored in this way is its vibra-tions. In this paper, a mathematical model of vibration analysis of a passenger car gearbox is presented based on Beta-Kurtosis and Kurtosis methods. In the next step, the data and test settings for the gearbox are based on the accelerometer installation to record the vibrations of the gearbox. To verify the accuracy of the proposed method, the results of the vibrational analysis of the car gearbox in four modes of a healthy gearbox, defective gearbox in the shaft end bearing, gear shaft failure on the gear shaft, simultaneous failure of the bearing and gearbox on the gear shaft were compared. Also, the results are compared for both Kurtosis and Beta-Kurtosis methods. The results show that both of the proposed methods are very accurate in identifying faults in the gearbox and determining the type of fault.

     

     


  • Keywords


    Gearbox; Troubleshooting; Beta-Kurtosis; Kurtosis; Vibrational Frequency Analysis

  • References


      [1] McFadden, P. D. (1989). Interpolation techniques for time domain averaging of gear vibration. Mechanical systems and signal processing, 3 (1), 87-97.

      [2] Futter, DN (1995). Techniques for monitoring the large gearboxes in the power industry. Insight, 37 (8), 591-594.

      [3] Wuxing, L., Peter, W. T., Guicai, Z. & Tielin, S. (2004). Classification of gear faults using cumulants and the radial base function network. Mechanical systems and signal processing, 18 (2), 381-389.

      [4] Yena, D.P., Sahoo, S., & Panigrahi, S.N. (2014). Gear fault diagnosis using active noise reduction and adaptive wavelet transform. Measurement 47: 356-372.

      [5] Asr, M. Y., Ettefagh, M. M., Hassannejad, R., & Razavi, S. N. (2017). Diagnosis of combined faults in Rotary Machines by Non-Naive Bayesian approach. Mechanical Systems and Signal Processing, 85, 56-70.

      [6] Andrew D. Dimarogon and Sam Haddad. (1992), "Vibration for Engineers." Machinary Vibration: Monitoring and Diagnosis 14,675-706.


 

View

Download

Article ID: 13068
 
DOI: 10.14419/ijet.v7i2.13.13068




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.