Data-driven torsional vibration-based fault diagnosis of large internal combustion engines without real fault data

Cylinder-specific condition monitoring is critical for ensuring reliability and safety in large combustion engines. However, conventional approaches typically require extensive instrumentation, resulting in high implementation costs and complexity. This paper presents a data-driven and sensor-efficient condition monitoring methodology for large industrial engines that achieves accurate fault detection using only a single flywheel encoder measurement. Despite the scarcity of real-world fault data, the framework leverages simulation-based training enhanced by domain randomization, feature alignment, and semi-supervised learning techniques to bridge the simulation-to-reality gap. A modified Deep Convolutional Neural Network with Wide First-layer Kernels (WDCNN) is employed for robust fault classification. The framework is validated on a 20-cylinder gas engine. Compared to conventional lateral vibration- or pressure-based monitoring systems that rely on distributed multi-sensor frameworks, this approach achieves comparable accuracy with drastically reduced sensor requirements, reducing the required amount from up to 40 to 1. A lower number of required sensors maintain higher reliability levels, since higher sensor counts expose the system to a higher rate of sensor failure. Experimental results show 100% fault detection accuracy and 95.7% classification accuracy on a dataset consisting of limited real measured data, highlighting the framework’s potential for practical deployment in real-world industrial settings with minimal sensor setups. Validation was conducted using a single real-world fault condition, underscoring the need for future validation on broader fault generalization. Nevertheless, this work demonstrates the feasibility of high-precision engine fault monitoring with dramatically reduced sensor requirements, enabling cost-effective diagnostics in data-scarce industrial environments.