Misfire detection in internal combustion engine with MEMS accelerometer using decision-tree classifiers

Detecting misfires in internal combustion engines (ICEs) is essential for maintaining engine health, reducing emissions, and improving performance. However, current misfire detection systems are often generic and lack the capability to localize faults to specific cylinder banks. This limitation is primarily due to the high cost of piezoelectric sensors and the complexity of associated data processing, which restricts widespread adoption, especially in cost-sensitive applications. To address this challenge, the present study explores the use of cost-effective microelectromechanical system (MEMS) sensors for real-time misfire detection. Vibration data from a production Hyundai Xcent MPFI engine is collected and analyzed using decision tree machine learning classifiers. Three types of features—statistical, auto-regressive moving average (ARMA), and histogram-based—are extracted from the MEMS data. The J48 decision tree classifier, when applied to selected histogram features, achieves 100.00% classification accuracy which in turn exudes its effectiveness in the detection and specific localization of misfires. This result is found to exceed the performance level investigated in studies with high classification accuracies averaging between 99.00% and 99.80%, including methodologies spanning transfer learning models for similar applications. This approach offers a low-cost, high-performance solution suitable for on-board engine diagnostics. Furthermore, this approach provides a framework that prospectively enables a broader integration of advanced misfire detection in ICE applications.