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The statistical relationship between sensor signature features and lubricant solid particle contamination conditions in a spherical roller bearing has been investigated in this study. The influence of particle size and concentration of solid contaminants in lubricant on the RMS parameter of time-domain acoustic emission, vibration, and sound sensor signals are examined. Machine learning algorithms are trained with time domain statistical features derived from sensor signatures to predict the lubricant conditions. Decision trees, bagging tree ensembles, and support vector machines are used to build ML models. Decision Tree models are built using classification and regression tree algorithms with three distinct split criteria, namely gini, towing, and maximum deviance. A bagged tree ensemble model is constructed using the decision tree as a base learner. In the support vector machine, kernel tricking is done to optimize the classification boundaries. Models built using Acoustic emission signature features predict lubricant conditions with better accuracy compared to models constructed using sound and vibration signature features. Feature-level fusion approach is implemented by combining the vibration, sound, and acoustic emission features at the feature level to improve the prediction power of machine learning models. The bagged tree ensemble and support vector machine models, which are trained using fused features, predict lubricant conditions in spherical roller bearings with an accuracy of around 99%. |
