Condition-based monitoring of HCCI engines utilizing vibration signals

Marine transportation carries about 80% of global cargo, making it vital to global logistics. Growing environmental and fuel-efficiency demands drive the need for cleaner, low-emission engines. Homogeneous charge compression ignition (HCCI) offers high thermal efficiency and minimal emissions but faces challenges in maintaining combustion stability under varying conditions. Conventional in-cylinder pressure-based control works well within narrow phasing ranges but lacks responsiveness during large deviations.This study presents a novel vibration-based framework for detecting combustion states in an HCCI single-cylinder engine. Vibration signals, recorded from the research unit, were processed to extract time-domain and frequency-domain features, which were then fed into a supervised classification model trained on combustion-phasing data (CA50). Combustion states—Normal, Late, and Very Late—were classified using K-nearest neighbors (KNN), support vector machines (SVM), and artificial neural networks (ANN). All models showed high accuracy, with F1-scores above 98% for most classes. The ANN achieved the best performance (98.3% accuracy) and demonstrates strong potential for robust, real-time combustion monitoring and control applications.