الملخص الإنجليزي
Abstract:
A model with high accuracy of machine failure prediction is important for any machine life cycle. In this paper,
a prediction model based on machine learning methods is proposed. The used method is a combination of
machine learning algorithms and techniques. The machine learning algorithm is a data mining technique that
has been widely used as a prediction model for classifying problems. Five algorithms have been tested including JRIP, logistic, KStar, Bayes network and decision table machine learning. The evaluation process is done
by applying the algorithms on a predictive dataset using different performance measures. In the proposed
model, the feature selection and voting techniques are used and applied in the classification process for each
classifier. From the comparison of the result, the feature selection shows the best performance result. Paired ttest evaluation measures were considered to confirm our conclusion. The best accuracy result among the five
classifiers shows that joint reserve intelligence classifier can be used to predict the failure with an accuracy
high as 0.983. Applying classifier subset evaluation using the JRIP classifier can enhance the accuracy result to
be 0.985. The finding shows that the proposed model improves the results of the classifiers.
