Steel Plates Faults Diagnosis with Data Mining Models

Abstract

Problem statement: Over the last two decades, Fault Diagnosis (FD) has a major importance to enhance the quality of manufacturing and to lessen the cost of product testing. Actually, quick and correct FD system helps to keep away from product quality problems and facilitates precautionary maintenance. FD may be considered as a pattern recognition problem. It has been gaining more and more attention to develop methods for improving the accuracy and efficiency of pattern recognition. Many computational tools and algorithms that have been recently developed could be used. Approach: This study evaluates the performances of three of the popular and effective data mining models to diagnose seven commonly occurring faults of the steel plate namely; Pastry, Z_Scratch, K_Scatch, Stains, Dirtiness, Bumps and Other_Faults. The models include C5.0 decision tree (C5.0 DT) with boosting, Multi Perception Neural Network (MLPNN) with pruning and Logistic Regression (LR) with step forward. The steel plates fault dataset investigated in this study is taken from the University of California at Irvine (UCI) machine learning repository. Results: Given a training set of such patterns, the individual model learned how to differentiate a new case in the domain. The diagnosis performances of the proposed models are presented using statistical accuracy, specificity and sensitivity. The diagnostic accuracy of the C5.0 decision tree with boosting algorithm has achieved a remarkable performance with 97.25 and 98.09% accuracy on training and test subset. C5.0 has outperformed the other two models. Conclusion: Experimental results showed that data mining algorithms in general and decision trees in particular have the great impact of on the problem of steel plates fault diagnosis.