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has been cited by the following article:

• TITLE: Intelligent Fault Detection of Retainer Clutch Mechanism of Tractor by ANFIS and Vibration Analysis

  AUTHORS: Ebrahim Ebrahimi, Payam Javadikia, Mohammad Hadi Jalili, Nasrolah Astan, Majid Haidari, Mojtaba Bavandpour

  KEYWORDS: Fault Detection; Maintenance Clutch Mechanism; Vibration Analysis; Neuro-Fuzzy Inference Systems

  JOURNAL NAME: Modern Mechanical Engineering, Vol.3 No.3A, July 18, 2013

  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.