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Failures of marine propulsion components or systems can lead to serious consequences for a vessel, cargo and the people onboard a ship. These consequences can be financial losses, delay in delivery time or a threat to safety of the people onboard. This is why it is necessary to learn about marine propulsion failures in order to prevent worst-case scenarios. This paper aims to provide a review of experimental, analytical and numerical methods used in the failure analysis of ship propulsion systems. In order to achieve that, the main causes and failure mechanisms are described and summarized. Commonly used experimental, numerical and analytical tools for failure analysis are given. Most indicative case studies of ship failures describe where the origin of failure lies in the ship propulsion failures (i.e., shaft lines, crankshaft, bearings, foundations). In order to learn from such failures, a holistic engineering approach is inevitable. This paper tries to give suggestions to improve existing design procedures with a goal of producing more reliable propulsion systems and taking care of operational conditions.

This study investigates the design and potential failure modes of a marine propeller shaft using computational and analytical methods. The aim is to assess the structural integrity of the existing design and propose modifications for improved reliability and service life. Analytical calculations based on classification society rules determined acceptable shaft diameter ranges, considering torsional shear stress limits for SAE 1030 steel. A Campbell diagram analysis identified potential resonance issues at propeller blade excitation frequencies, leading to a recommended operating speed reduction for a safety margin. Support spacing was determined using both the Ship Vibration Design Guide and an empirical method, with the former yielding more conservative results. Finite element analysis, focusing on the keyway area, revealed stress concentrations approaching the material’s ultimate strength. A mesh sensitivity analysis ensured accurate stress predictions. A round-ended rectangular key geometry modification showed a significant stress reduction. Fatigue life analysis using the Goodman equation, incorporating various factors, predicted infinite life under different loading conditions, but varying safety factors highlighted the impact of these conditions. The FEA revealed that the original keyway design led to stress concentrations exceeding allowable limits, correlating with potential shaft failure. The proposed round-ended rectangular key geometry significantly reduced stress, mitigating the risk of fatigue crack initiation. This research contributes to the development of more reliable marine propulsion systems by demonstrating the efficacy of integrating analytical methods, finite element simulations, and fatigue life predictions in the design process.

The reliability of marine propulsion systems depends on the reliability of several sub-systems of a diesel engine. The scavenge air system is one of the crucial sub-systems of the marine engine with a turbocharger as an essential component. In this paper, the failures of a turbocharger are analyzed through the fault tree analysis (FTA) method to estimate the reliability of the system and to predict the cause of failures. The quantitative method is used for assessing the probability of faults occurring in the turbocharger system. The main failures of a scavenge air sub-system, such as air filter blockage, compressor fouling, turbine fouling (exhaust side), cooler tube blockage and cooler air side blockage, are simulated on a Wärtsilä-Transas engine simulator for a marine two-stroke diesel engine. The results obtained through the simulation can provide improvement in the maintenance plan, reliability of the propulsion system and optimization of turbocharger operation during exploitation time.

With the IMO’s 2050 decarbonization target, hydrogen is a key zero-carbon fuel for shipping, but the lack of systematic risk assessment methods for hydrogen-powered marine propulsion systems (under harsh marine conditions) hinders its large-scale application. To address this gap, this study proposes an integrated risk evaluation framework by fusing Failure Mode, Effects, and Criticality Analysis (FMECA) with the Fuzzy Analytic Hierarchy Process (FAHP)—resolving the limitation of traditional single evaluation tools and adapting to the dynamic complexity of marine environments. Scientific findings from this framework confirm that hydrogen leakage, high-pressure storage tank valve leakage, and inverter overload are the three most critical failure modes, with hydrogen leakage being the primary risk source due to its high severity and detection difficulty. Further hazard matrix analysis reveals two key risk mechanisms: one type of failure (e.g., insufficient hydrogen concentration) features “high severity but low detectability,” requiring real-time monitoring; the other (e.g., distribution board tripping) shows “high frequency but controllable impact,” calling for optimized operations. This classification provides a theoretical basis for precise risk prevention. Targeted improvement measures (e.g., dual-sealed valves, redundant cooling circuits, AI-based regulation) are proposed and quantitatively validated, reducing the system’s overall risk value from 4.8 (moderate risk) to 1.8 (low risk). This study’s core contribution lies in developing a universally applicable scientific framework for marine hydrogen propulsion system risk assessment. It not only fills the methodological gap in traditional evaluations but also provides a theoretical basis for the safe promotion of hydrogen shipping, supporting the scientific realization of the IMO’s decarbonization goal.

The transition to environmentally sustainable maritime operations has gained urgency with the International Maritime Organization’s (IMO) 2023 GHG reduction strategy, aiming for net-zero emissions by 2050. While alternative fuels like LNG and methanol serve as transitional solutions, lithium-ion battery energy storage systems (ESSs) offer a viable low-emission alternative. However, safety concerns such as thermal runaway, overcharging, and internal faults pose significant risks to marine battery systems. This study presents an AI-based fault prediction algorithm to enhance the safety and reliability of lithium-ion battery systems used in electric propulsion ships. The research employs a Long Short-Term Memory (LSTM)-based predictive model, integrating electrochemical impedance spectroscopy (EIS) data and voltage deviation analyses to identify failure patterns. Bayesian optimization is applied to fine-tune hyperparameters, ensuring high predictive accuracy. Additionally, a recursive multi-step prediction model is developed to anticipate long-term battery performance trends. The proposed algorithm effectively detects voltage deviations and pre-emptively predicts battery failures, mitigating fire hazards and ensuring operational stability. The findings support the development of safer and more reliable energy storage solutions, contributing to the broader adoption of electric propulsion in maritime applications.

The present study is devoted to developing a digital twin for a composite overwrapped pressure vessel (COPV) used in electric propulsion engines of spacecraft. The digital twin is used to predict the future behavior and performance of a real physical object based on the currently available information without carrying out expensive and time-consuming full-scale prototyping and testing. Multiscale approach is employed to link the macroscopic stiffness degradation and failure with a progressive damage evolution at the microlevel of composite. The computational models for the stress state and failure analysis at different scale levels are presented. Based on a comparative analysis of the traditional approach for assessing the load-bearing capacity of the COPV and its digital analogue, the advantages of the latter are shown as the predicted burst pressure is in good agreement with the experimental results.

This study presents a comprehensive finite element investigation into the design optimization of an ultra-high temperature ceramic matrix composite thruster for green bipropellant systems. Focusing on ZrB2–SiC–Cfiber composites, it explores their thermal and mechanical response under realistic transient combustion conditions. Two geometries, a simplified and a complex full-featured model, were evaluated to assess the impact of geometric fidelity on stress prediction. The complex thruster model (CTM) offered improved resolution of temperature gradients and stress concentrations, especially near flange and convergent regions, and was adopted for optimization. A parametric study with nine wall thickness profiles identified a 2 mm tapered configuration in both convergent and divergent sections that minimized mass while maintaining structural integrity. This optimized profile reduced peak thermal stress and overall mass without compromising safety margins. Transient thermal and strain analyses showed that thermal stress dominates initially (≤3 s), while thermal strain becomes critical later due to stiffness degradation. Damage risk was evaluated using temperature-dependent stress margins at four critical locations. Time-dependent failure maps revealed throat degradation for short burns and flange cracking for longer durations. All analyses were conducted under hot-fire conditions without cooling. The validated methodology supports durable, lightweight nozzle designs for future green propulsion missions.

To enhance the reliability of electric propulsion in electric aircraft and address power interruptions caused by current sensor failures, this study proposes a current sensorless fault-tolerant control strategy for permanent magnet synchronous motors (PMSMs) based on a long short-term memory (LSTM) network. First, a hierarchical architecture is constructed to fuse multi-phase electrical signals in the fault diagnosis layer (sliding mode observer). A symbolic function for the reaching law observer is designed based on Lyapunov theory, in order to generate current predictions for fault diagnosis. Second, when a fault occurs, the system switches to the LSTM reconstruction layer. Finally, gating units are used to model nonlinear dynamics to achieve direct mapping of speed/position to phase current. Verification using a physical prototype shows that the proposed method can complete mode switching within 10 ms after a sensor failure, which is 80% faster than EKF, and its speed error is less than 2.5%, fully meeting the high speed error requirements of electric aircraft propulsion systems (i.e., ≤3%). The current reconstruction RMSE is reduced by more than 50% compared with that of the EKF, which ensures continuous and reliable control while maintaining the stable operation of the motor and realizing rapid switching. The intelligent algorithm and sliding mode control fusion strategy meet the requirements of high real-time performance and provide a highly reliable fault-tolerant scheme for electric aircraft propulsion.

Autonomous navigation technologies have been widely adopted in the automotive and aviation sectors, significantly reducing human-error-induced accidents and operational costs. However, their application to maritime systems remains limited due to the complexity of conventional propulsion systems. Electric propulsion ships, with well-defined system boundaries and accessible operational data, offer a promising platform for autonomous navigation. In this study, we propose an FMEA-guided selective multi-fidelity digital twin framework for fault detection, where model fidelity is adaptively selected between low- and high-fidelity models based on risk priority numbers derived from failure mode and effects analysis. This approach enables selective execution of computationally expensive models only under high-risk conditions, thereby improving computational efficiency. In addition, a sliding window-based algebraic aggregation method is employed to achieve lightweight and real-time fault diagnosis. The proposed framework is validated using operational sensor data from a 100 kW electric propulsion ship under multiple fault scenarios, including power supply faults and signal anomalies. Experimental results show that the proposed method reduces computational cost while maintaining stable real-time performance, compared to conventional data-driven AI-based approaches. These results demonstrate that the proposed framework provides an effective and efficient solution for enhancing the reliability and safety of autonomous ship systems.

The development of artificial intelligence-based tools is having a big impact on industry. In this context, the maintenance operations of important assets and industrial resources are changing, both from a theoretical and a practical perspective. Namely, conventional maintenance reacts to faults and breakdowns as they occur or schedules the necessary inspections of systems and their parts at fixed times by using statistics on component failures, but this can be improved by a predictive maintenance based on the real component’s health status, which is inspected by appropriate sensors. In this way, maintenance time and costs are saved. Improvements can be achieved even in the marine industry, in which complex ship propulsion systems are produced for operation in many different scenarios. In more detail, data-driven models, through machine learning (ML) algorithms, generate the expected values of monitored variables for comparison with real measurements on the asset, for a diagnosis based on the difference between expectations and observations. The first step towards realization of predictive maintenance is choosing the ML algorithm. This selection is often not the consequence of an in-depth analysis of the different algorithms available in the literature. For that reason, here the authors propose a framework to support an initial implementation stage of predictive maintenance based on a benchmarking of the most suitable ML algorithms. The comparison is tested to predict failures of the oil circuit in a diesel marine engine as a case study. The algorithms are compared by considering not only the mean squared error between the algorithm predictions and the data, but also the response time, which is a crucial variable for maintenance. The results clearly indicate the framework well supports predictive maintenance and the prediction error and running time are appropriate variables to choose the most suitable ML algorithm for prediction. Moreover, the proposed framework can be used to test different algorithms, on the basis of more performance indexes, and to apply predictive maintenance to other engine components.