Developing an Intelligent Fault Diagnosis of MF285 Tractor Gearbox Using Genetic Algorithm and Vibration Signals
- 1 Department of Mechanical Engineering of Agricultural Machinery, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
- 2 Department of Mechanical Engineering of Agricultural Machinery, Razi University, Kermanshah, Iran
- 3 Department of Mechanical Engineering of Agricultural Machinery, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
- 4 Department of Mechanical Engineering of Agricultural Machinery, Takestan Branch, Islamic Azad University, Takestan, Iran
- 5 Department of Mechanical Engineering of Agricultural Machinery, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
- 6 Department of Mechanical Engineering of Agricultural Machinery, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
- 7 Department of Mechanical Engineering of Agricultural Machinery, East Azarbaijan Sience and Research Branch, Islamic Azad University, Tabriz, Iran
Abstract
This article investigates a fault detection system of MF285 Tractor gearbox empirically. After designing and construct ing 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 de signed 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 coef ficient of the network to adapt to the mechanism of defect detection and classification system is equal to 99.9%.
- 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
- 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
- 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.
- 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.
- N. Hotwai, “Vibration Analysis of Faulty Beam usig Fuzzy Logic Techique,” BTech Thesis, National Institute of Technology, Rourkela, 2009, pp. 23-27.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.