Application of Neural Networks in Fault Classification of a kind of Clutch Mechanism Retainer

The purpose of this study is to introduce an intelligent method for fault classification of farm machinery with vibration analysis using the ANFIS. Test conditions include safe, roller fault, seal fault and axis friction on the experimental setup of retainer and clutch mechanism of Massey Ferguson. The time-domain vibration signals with normal and defective modes processed for feature extraction. Three features of the time domain (T11), frequency domain (A8) and phase angle (A9) data as premium features were selected. Data are classified into eight experimental models and also loaded in the ANFS network. In all models, appropriate membership functions were selected. For ANFIS training 1000 epoch was considered. The statistical indicators and the result of ANFIS prediction of evaluation models were presented. Total classification accuracy was 100% in both models. The results showed that ANFIS can be used as a powerful tool for intelligent fault classification of tractor mechanisms.

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