<div>Various internal combustion (IC) engine condition monitoring techniques exist for
early fault detection and diagnosis to ensure smooth operation, increased
durability, low emissions, and prevent breakdowns. A fault, such as piston slap,
can damage critical components like the piston, piston rings, and cylinder liner
and is among those faults that may lead to such consequences. This research has
been conducted to monitor piston slap conditions by analyzing the engine
vibration and acoustic emission (AE) signals. An experimental setup has been
established for acquiring vibration and AE sensor signatures for various piston
slap severity conditions. Time-domain features are extracted from vibration and
AE sensor signatures, and among them, the best features are selected using
one-way analysis of variance (ANOVA) to create machine learning (ML) models.
Apart from individual sensor feature classification, the feature fusion method
increases the prediction accuracy. ML algorithms used in this study for building
the prediction models are classification and regression trees (CART), random
forest, and support vector machine (SVM). Performance comparisons of these
trained models are made using different performance measures. It is observed
that about 94.95% of maximum classification accuracy is obtained in predicting
the piston slap severity at different speeds and load conditions.</div>