Condition-based monitoring of HCCI engines utilizing vibration signals
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Hasanzadeh Shadiani, A., Nasajian Moghaddam, A.-H., Shamekhi, A.-M., Basher, A., Hunicz, J., Boutellier, J., & Mikulski, M. (2026). Condition-based monitoring of HCCI engines utilizing vibration signals. Measurement and Control. https://doi.org/10.1177/00202940261469270
© The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permissionprovided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Lataukset7
Pysyvä osoite
Kuvaus
Marine transportation moves approximately 80% of the world’s cargo volume, playing a vital role in global logistics. Ever-increasing environmental challenges and fuel economy concerns demand efficient, low-emission engine technologies. Homogeneous charge compression ignition (HCCI) offers improved thermal efficiency at minimum engine-out emissions, but maintaining combustion stability across varying operating conditions limits its widespread adoption. Established in-cylinder pressure-based control systems perform reliably when combustion phasing variations remain small but lack responsiveness under larger deviations, limiting robust real-time combustion state detection and correction. This study presents a vibration-based sensing and classification framework for detecting combustion states in an HCCI engine. Vibration signals recorded from a single-cylinder HCCI research engine were processed to extract a compact set of time- and frequency-domain features, which were used to train supervised classification models based on combustion phasing (CA50). Combustion states were categorized as Normal, Late, or Very Late Combustion. Six machine learning models were evaluated: K-nearest neighbors (KNN), support vector machines (SVM), Artificial neural networks (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and a convolutional neural network (CNN) as a deep learning benchmark. All models demonstrated high accuracy and robustness, achieving F1-scores exceeding 98% for the Normal and Very Late Combustion classes. The ANN achieved the highest test accuracy of 98.36%, outperforming the CNN benchmark, particularly for the challenging Late Combustion class, demonstrating the effectiveness of the physically informed feature-based approach. The performance for the Late Combustion class was slightly lower, but the method remains promising for real-time applications due to its minimal computational overhead.
Emojulkaisu
ISBN
ISSN
2051-8730
0020-2940
0020-2940
Aihealue
Kausijulkaisu
Measurement and control
OKM-julkaisutyyppi
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä (vertaisarvioitu)