Share This Article:

Developing an Intelligent Fault Diagnosis of MF285 Tractor Gearbox Using Genetic Algorithm and Vibration Signals

Abstract Full-Text HTML XML Download Download as PDF (Size:907KB) PP. 152-160
DOI: 10.4236/mme.2013.34022    4,391 Downloads   7,507 Views   Citations

ABSTRACT

This article investigates a fault detection system of MF285 Tractor gearbox empirically. After designing and constructing the laboratory set up, the vibration signals obtained using a Piezoelectric accelerometer which has been installed on the Bearing housings are related to rotary gear number 1 in two directions perpendicular to the shaft and in line with the shaft. The vector data were conducted in three different speeds of shaft 1500, 1000 and 2000 rpm and 130 repetitions were performed for each data vector state to increase the precision of neural network by using more data. Data captured were transformed to frequency domain for analyzing and input to the neural network by Fourier transform. To do neural network analysis, significant features were selected using a genetic algorithm and compatible neural network was designed with data captured. According to the results of the best output mode for each position of the sensor network in 1000, 1500 and 2000 rpm, totally for the six output models, all function parameters for MATLAB Software quality content calculated to evaluate network performance. These experiments showed that the overall mean correlation coefficient of the network to adapt to the mechanism of defect detection and classification system is equal to 99.9%.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Ebrahimi, E. , Javadikia, P. , Astan, N. , Heydari, M. , Bavandpour, M. , Jalili, M. and Zarei, A. (2013) Developing an Intelligent Fault Diagnosis of MF285 Tractor Gearbox Using Genetic Algorithm and Vibration Signals. Modern Mechanical Engineering, 3, 152-160. doi: 10.4236/mme.2013.34022.

References

[1] M. Elforjani, D. Mba, A. Muhammad and A. Sire “Condition Monitoring of Worm Gears,” Applied Acoustics, Vol. 73 No. 8, 2012, pp. 859-863.
http://dx.doi.org/10.1016/j.apacoust.2012.03.008
[2] H.-E. Kim, A. C. C. Tan, J. Mathew and B.-K. Choi, “Bearing Fault Prognosis Based on Health State Probability Estimation,” Expert Systems with Applications, Vol. 39, No. 5, 2012, pp. 5200-5213.
http://dx.doi.org/10.1016/j.eswa.2011.11.019
[3] P. J. Dempsey, D. G. Lewicki, and H. J. Decker, “Decker Transmission Bearing Damage Detection Using Decision Fusion Analysis,” Glenn Research Center, Cleveland, Army Research Laboratory, NASA/TM, 2004, pp. 1-20.
[4] G. Goddu, B. Li, M. Chow and J. Hung, “Motor Bearing Fault Diagnosis by Fundamental Frequency Amplitude Based Fuzzy Decision System,” North Carolina University. 1998, pp. 1961-1965.
[5] N. Hotwai, “Vibration Analysis of Faulty Beam usig Fuzzy Logic Techique,” BTech Thesis, National Institute of Technology, Rourkela, 2009, pp. 23-27.
[6] Ch. Kong, J. Ki, S. Oh and J. Kim, “Trend Monitoring of a Turbofan Engine for Long Endurance UAV Using Fuzzy Logic,” KSAS Enternational Gournal, Vol. 9. 2008, pp. 64-70.
[7] Z. Kiral and H. Karagulle, “Simulation and Analysis of Vibration Signals Generated by Rolling Element Bearing with Defects,” Tribology International, Vol. 36, No. 9, 2003, pp. 667-678.
[8] Y. G. Lei, Z. J. He and Y. Y. Zi and X. Hu, “Fault Diagnosis of Rotating Machinery Based on Multiple ANFIS Combination with Gas,” Mechanical Systems and Signal Processing, Vol. 21, No. 5, 2007, pp. 2280-2294.
[9] Y. G. Lei, Z. J. He and Y. Y. Zi, “A New Approach to Intelligent Fault Diagnosis of Rotating Machinery,” Expert Systems with Applications, Vol. 35, No. 4, 2008, pp. 1593-1600.
[10] R. Martins Marcal, K. Hatakeyama and A. Susin, “Managing Incipient Faults in Rotating Machines Based on Vibration Analysis and Fuzzy Logic,” Electrical Engineering Department, 2006, pp. 1-6.
[11] N. Saravanan, S. Cholairajan and K. I. Ramachandran “Vibration-based Fault Diagnosis of Spur Bevel Gear box Using Fuzzy Technique,” Expert Systems with Applications, Vol. 36, No. 2, 2009, pp. 3119-3135.
[12] T. I. Liua, J. H. Singonahallib and N. R. Iyerb, “Detection of Roller Bearing Defect Using Expert System and Fuzzy Logic,” Mechanical Systems and Signal Processing, Vol. 10, No. 5, 1996, pp. 595-614.
[13] E. Ebrahimi and K. Mollazade, “Intelligent Fault Classification of a Tractor Starter Motor Using Vibration Monitoring and Adaptive Neuro-Fuzzy Inference System,” Insight—Non-Destructive Testing and Condition Monitoring, Vol. 52, No. 10, 2010, pp. 561-566.
[14] G. H. Payganeh, M. N. Khajavi, R. Ebrahimpour, and E. Babaei, “Troubleshooting of Rotating Machines Using Neural Networks,” 6th National Conference on Maintenance and Repair, Tehran, Iran.
[15] A. Bavi and M. Salehi, “Genetic Algorithms and Optimization of Composite Structures,” Tehran, Abed.
[16] D. Boostan and N. Pariz, “Analysis of Vibration Signals Using Short Time Fourier Transform, Neural Networks and Fuzzy Inference to Identify the Type and Severity of Damage in the Bearing,” 16th Annual International Conference on Mechanical Engineering, Tehran, Shahid Bahonar University.
[17] M. Behzad, K. Sepanloo, M. Asayesh and E. Rohani, “Fundamentals of Vibrations in the Maintenance, Repair and Troubleshooting of Rotating Machinery,” NPC Publication.
[18] H. Beigy, “MF 285 Vibrating Gearbox Condition Monitoring and Fault Features and Its Classification Using Artificial Neural Networks,” M.Sc. Thesis, Tehran University.
[19] E. Rohani, M. Alikhani, E. Mostafavi Manesh, M. R. Darbandi and M. E. A. Behzad, “Troubleshooting by the Help of Turbo Compressors Gas Pipeline Vibration Analysis, Condition Monitoring and Diagnostics of Machines,” 4th Conference of Iran, Sharif University of Technology, Tehran.
[20] M. Ryahi and A. Tarbareh, “Designing and Manufacturing the System of Information Recording and Preserving on Heavy Machinery to Monitor and Analyze the Status, Condition Monitoring and Diagnostics of Machines,” 4th Conference of Iran, Sharif University of Technology, Tehran.
[21] B. V. Zahraei and M. Hosseyni, “Genetic Algorithms and Engineering Optimization,” Tehran, Gothenbur.
[22] M. Shakeri and M. Nozad, “Remote Diagnostics of Rotating Machineries Based on Vibration Condition Monitoring and Troubleshooting of Machines,” 4th Technical Conference and Equipment of Iran, Sharif University of Technology, Tehran.
[23] E. Ghafar Nejad Mehraban and M. Homayoon Sadeghi, “Smart Troubleshooting for Car Gearbox Vibration Using Analysis and Artificial Neural Networks by Analyzing Violet,” 16th Annual Conference on Mechanical Engineering.
[24] A. Farshidian Far, S. Maleki and E. Andakhshideh, “Vortex-Induced Vibration Modeling and Optimization of Cylinders by a Neural Network and Genetic Algorithm,” 7th Annual Conference of the International Association of Aerospace Iran, Sharif University of Technology, Tehran.
[25] M. Kia, “Neural networks in Matlab,” Kian Publication of Rayaneh Sabz, Tehran.
[26] H. Mohammadi Monavar, “Vibration Condition Monitoring in Agriculture Electro-Motors with Fans,” Master’s Thesis, Tehran University, Tehran.
[27] K. MollaZadeh, “Vibration Condition Monitoring of Hydraulic Steering Pump MF 285 and the Defects Classification Using Fuzzy Logic,” Master’s Thesis, Tehran University, Tehran.

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.