Condition Monitoring of Two Stroke IC Engine Ball Bearing Based on Vibration Signals Using Machine Learning Techniques

Abstract

Ball bearing faults are foremost cause to the breakdown of rotating machine components. In general, ball bearing in the IC engine is one of the vital machine elements and plays major role in the rotating machine as it takes different kinds of stresses and dynamic loads under various working conditions. Monitoring of such ball bearing continuously is extremely needed to avoid damage to the components of IC Engine. Vibration analysis of IC engine is most commonly used to recognise the fault in ball bearing and to differentiate the types of ball bearing defects. In the present work, study has been conducted on condition monitoring of ball bearing of 2-stroke single cylinder IC engine using vibration signals and explains the use of statistical features and support vector machine as a tool for classification of the ball bearing conditions. The different cases of faults considered in the diagnosis of ball bearing are outer race (OR) defect, inner race (IR) defect, combined faults in outer and inner race (OR & IR) and ball defect. The vibration signals under good and different simulated defective conditions of ball bearing are collected using an accelerometer. The descriptive statistical set of features are found from the collected vibration signals. The significant features are carefully chosen by means of J48 decision tree technique and the features chosen from tree are given as input to the support vector machine (SVM). The experimental results show that, the SVM model provides 96% classification accuracy for the given vibration signals. The obtained result from the model is good and can be considered for diagnosis of rotating machinery. Hence, the J48 decision tree and SVM algorithm can be proposed for applications of condition monitoring of ball bearings.