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This paper presents an intelligent method for fault diagnosis of the starter motor of an agricultural tractor, based on vibration signals and an Adaptive Neuro-Fuzzy Inference System (ANFIS). The starter motor conditions to be considered were healthy, crack in rotor body, unbalancing in driven shaft and wear in bearing. Thirty-three statistical parameters of vibration signals in the time and frequency domains were selected as a feature source for fault diagnosis. A data mining filtering method was performed in order to extract the superior features among the primary thirtythree features for the classification process and to reduce the dimension of features. In this study, six superior features were fed into an adaptive neuro-fuzzy inference system as input vectors. Performance of the system was validated by applying the testing data set to the trained ANFIS model. According to the result, total classification accuracy was 86.67%. This shows that the system has great potential to serve as an intelligent fault diagnosis system in real applications.

In this paper, a novel approach to classify the potato tubers based on their moisture content has been proposed using image processing and multilayer perceptron (MLP) neural network. Some experiments were conducted on 300 independent potato samples during three storing stages. Images of 576×768 pixel sizes from potato samples, with three different moisture contents, were captured using a color CCD camera. After preprocessing and segmentation, 84 features were extracted from the acquired images. Sensitivity analysis was used for feature selection process. Results of feature reduction showed that features in color spaces are more important than those in texture and fast Fourier transform (FFT). A process of trial-and-test procedure was carried out to find the optimum topology, the number of hidden layers and the number of neurons in hidden layers, of MLP network. Results obtained from the final ANN models showed encouraging accuracy (more than 96.22%) to apply the approach to develop an expert system for online potato quality monitoring.