Physics-Based Modelling for On-Line Condition Monitoring of a Marine Engine System

The engine system is critical for a marine vehicle, and its performance significantly affects the efficiency and safety of the whole ship. Due to the harsh working environment and the complex system structure, a marine system is prone to have many kinds of novelties and faults. Timely detection of faults via effective condition monitoring is vital for such systems, avoiding serious damage and economic loss. However, it is difficult to realize online monitoring because of the limitations of measurement and health monitoring methods. In this paper, a marine engine system simulator is set up with enhanced sensory placement for static and dynamic data collection. The test rig and processing for static and dynamic data are described. Then, a physics-based multivariate modeling method is proposed for the health monitoring of the system. Case studies are carried out considering the misfire fault and the exhaust valve leakage fault. In the misfire fault test, the exhaust gas temperature of the misfired cylinder dropped from the confidence interval 100-150 & DEG;C to 70-80 & DEG;C and the head vibration features decreased from the confidence interval 900-1300 m/s(2) to around 200-300 m/s(2). For the exhaust valve leakage fault, the engine body vibration main bearing impact RMS increased nearly 10 times. Comparisons between the model-predicted confidence interval and measured data reveal that the proposed model based on the fault-related static and dynamic features successfully identified the two faults and their positions, proving the effectiveness of the proposed framework.

Automated summary

The engine system of a marine vehicle plays a critical role in its efficiency and safety. However, due to harsh working conditions and complex structures, marine systems are prone to various novelties and faults. This paper proposes a marine engine system simulator with enhanced sensory placement for data collection and a physics-based multivariate modeling method for health monitoring. Case studies on misfire and exhaust valve leakage faults demonstrate the effectiveness of the proposed framework in fault identification and localization.