Intelligent Fault Detection of Retainer Clutch Mechanism of Tractor by ANFIS and Vibration Analysis
- 1 Department of Mechanical Engineering of Agricultural Machinery, Islamic Azad University, Kermanshah Branch, Kermanshah, Iran
- 2 Department of Mechanical Engineering of Agricultural Machinery, Razi University, Kermanshah, Iran
- 3 Department of Mechanical Engineering of Agricultural Machinery, Islamic Azad University, Kermanshah Branch, Kermanshah, Iran
- 4 Department of Mechanical Engineering of Agricultural Machinery, Islamic Azad University, Kermanshah Branch, Kermanshah, Iran
- 5 Department of Mechanical Engineering of Agricultural Machinery, Islamic Azad University, Takestan Branch, Takestan, Iran
- 6 Department of Mechanical Engineering of Agricultural Machinery, Islamic Azad University, Kermanshah Branch, Kermanshah, Iran
Abstract
In this study, ANFIS, as decision support system, is applied to detect the faults of MF 285 mechanism tractor clutch. Maintenance mechanisms include normal mode, rolling element failure, seal failure and attrition-based. Experiments were carried out in three speeds: 1000, 15 , 000, 2000 RPM and two conditions. The sensor was mounted vertically and horizontally. Vibrating spectrum of the time domain and the frequency of vibration data were obtained. Thirty-three statistical parameters of vibration signals in frequency domain and time were chosen as the sources attribute to detect errors. Finally, the top three features as input vectors to the ANFIS were evaluated. Using statistical parameters the performance of the system was calculated with the experimental data and training of ANFIS model. The system could not provide a seal to identify the fault. Regardless of the vibration data obtained from the classification of the seal, the overall classification accuracy of the ANFIS was 99.14% in the amount of 100% of the sensor installed vertically and horizontally. The results showed that this system can be used as an intelligent diagnosis system.
- M. Elforjani, D. Mba, A. Muhammad and A. Sire, “Condition Monitoring of Worm Gears,” Applied Acoustics, Vol. 73, No. 8, 2012, pp. 859-863. doi: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. doi:10.1016/j.eswa.2011.11.019
- D. G. Lewicki and H. J. 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,” Development of Electrical and Computer Engineering, North Carolina University, 1998, pp. 1961-1965.
- N. Hotwai, “Vibration Analysis of Faulty Beam Using Fuzzy Logic Techique,” Department of Mechanical Engineering 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 International Journal, Vol. 9, 2008, pp. 64-70.
- Z. Kiral and H. Karagülle, “Simulation and Analysis of Vibration Signals Generated by Rolling Element Bearing with Defects,” Department of Mechanical Engineering, Dokuz Eylul University, Turkey, 2003, pp. 667-678.
- Y. Lei, Z. Hea, Y. Zia and Q. Hua, “Fault Diagnosis of Rotating Machinery Based on Multiple ANFIS Combination with Gas,” Department of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 2003, pp. 667-678.
- Y. A. Lei, Z. He and Y. Zi, “A New Approach to Intelligent Fault Diagnosis of Rotating Machinery,” Expert Systems with Applications, 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 and S. Cholairajan, “Ramachandran Vibration-Based Fault Diagnosis of Spur Bevel Gear Box Using Fuzzy Technique,” Department of Mechanical Engineering, Amrita Vishwa Vidyapeetham, India, 2009, pp. 3119-3135.
- J. H. Singonahalli and N. R. Iyer, “Etection of Roller Bearing Defect Using Expert System and Fuzzy Logic,” Department of Mechanical Engineering, California University Sacramento, Sacramento, 1996, pp. 596-613.