Search Results (34)

With the long-term operation of ships, the performance of marine diesel engines gradually declines due to the wear of internal moving components, increasing the risk of potential failures. Fuel consumption is a critical indicator for assessing engine operating conditions, and accurately predicting baseline fuel consumption under normal operating conditions is essential for evaluating ship energy efficiency and conducting fault diagnosis. To address common issues in marine engine operational data, such as noise pollution, missing values, inconsistent scales, and feature redundancy, a Diesel Engine Data Enhancement and Optimization Framework (DEOF) was developed to systematically improve data quality. Furthermore, to overcome the limitations of existing models, such as insufficient prediction accuracy and poor stability under complex operating conditions, a Meta-learning Diffusion Residual Attention Network (MD-RAN) is proposed. This approach leverages the strengths of diffusion models in nonlinear generative modeling, integrates meta-learning mechanisms to enhance task adaptation speed, employs multi-head attention modules to strengthen feature extraction, and incorporates dynamic residual connections to improve training stability and flexibility. The data used in this study were collected from real-world operations of ocean-going vessels, ensuring high representativeness. This paper systematically benchmarks the proposed model with the traditional learning model. The results are verified to be effective. The MD-RAN algorithm is significantly better than the original model in terms of prediction accuracy, stability, and nonlinear expression ability. The R² value can reach 0.9853, and the RMSE and MAE are as low as 1.5801 and 1.1879, respectively. Its feasibility will be further evaluated in practical applications in the future. This study not only provides a systematic data-driven modeling framework, offering technical insights for constructing high-quality datasets, but also establishes a novel generative modeling approach for marine diesel engine fuel consumption prediction, providing robust support for intelligent engine maintenance and energy efficiency optimization. Full article

As the core equipment in ship power systems, the accurate and real-time diagnosis of ship diesel engine faults directly affects navigation safety and operation efficiency. Existing methods (e.g., expert systems, traditional machine learning) can hardly cope with the complex failure modes and dynamic operation environment due to the problems of relying on artificial features and insufficient generalization ability. In this paper, we propose a BiLSTM-CRF-based knowledge graph construction method for ship diesel engine faults, aiming at integrating multi-source heterogeneous data through deep learning and knowledge graph technology, and mining the deep semantic associations among fault phenomena, causes, and solutions. The research framework covers data acquisition, ontology modeling, and knowledge extraction and storage, and the BiLSTM-CRF model is used to fuse bi-directional contextual features with label transfer probability to achieve high-precision entity recognition and relationship extraction. Finally, a scalable knowledge graph is constructed by Neo4j. Experiments show that the model significantly outperforms baseline methods such as HMM, CRF, and BiLSTM, and the graph visualization clearly presents the fault causality network, which supports knowledge reasoning and decision optimization. For example, “high exhaust temperature” can be related to potential causes such as “turbine failure” and “poor cooling”, and recommended measures can be taken. This method not only improves fault diagnosis accuracy and efficiency but also provides a novel method for intelligent ship health management. Full article

Polymer composite coatings exhibit excellent mechanical properties, chemical resistance, and self-lubricating characteristics, providing an effective solution to address the failure of transmission components under harsh operating conditions, including high-speed, high-pressure, and oil-deficient environments, which often lead to excessive friction and limited bearing performance. This study fabricated three polyamide-imide (PAI) composite coatings modified with monodisperse surface-modified nano-silica (SiO₂) via direct spraying and compared their physicochemical parameters. The tribological performance of the three coatings was evaluated using ring-block high-speed friction and wear tester under continuous loading conditions. The tests were conducted using diesel engine oil CI4-5W40, supplemented with oil-soluble cerium dioxide (CeO₂) nanoparticles as an energy-efficient and restorative additive, as the lubricating medium. The experimental results demonstrated that the PAI composite coating exhibited a load-bearing capacity exceeding 1000 N (66 MPa). The wear mechanism analysis reveals that CeO₂ nanoparticles embedded in the coating surface form a cobblestone-like protective layer. This unique microstructure compensates for the surface pits generated by PAI matrix transfer and minimizes direct contact between the coating and steel ring. Additionally, the synergistic interaction between short carbon fiber (SCF) and the tribofilm contributes to the exceptional tribological properties of the coating, including coefficients of friction as low as 0.04 and wear rates below 0.41 × 10⁻⁸ mm³/N·m. The experimental findings could provide an experimental and theoretical foundation for the application of coatings under conditions involving finished lubricants. Full article

Thermoeconomic diagnosis methods are designed to identify faulty components and evaluate the economic implications of these faults. However, these diagnostic techniques often struggle to filter out interference from induced factors during the diagnosis process. When multiple components malfunction simultaneously, these methods may fail to effectively identify all the faulty components. To address these challenges, this article introduces an improved thermoeconomic diagnosis method that integrates the traditional diagnosis method with the operational characteristic curves of the components. This improved method facilitates a more precise differentiation between the impacts of faults on each component, categorizing them into intrinsic and induced parts. The intrinsic part arises from the component’s inherent failure, while the induced part results from interactions among different components or adjustments made by the control system. The improved method generates fault diagnosis indicators and economic assessment indicators based on this classification, allowing for the identification of faulty components and the evaluation of the economic consequences of these faults. The proposed method was tested on a MAN 6S50 MC-C8 diesel engine and validated under two real operating conditions, where multiple faults were intentionally introduced in various components. The results demonstrated that the new method accurately identified all faulty components within the marine diesel engine and assessed the economic impacts of these faults. Full article

Ship diesel generator systems are critical to ship navigation. However, due to the harsh marine environment, the systems are prone to failures, and traditional fault diagnosis methods are difficult to meet requirements regarding accuracy, robustness, and reliability. For this reason, this paper proposes a fault diagnosis method for a ship diesel generator system based on the IVY algorithm-optimized random forest (IVY-RF). Firstly, a model of a ship diesel generator system was constructed using MATLAB/Simulink, and the operation data under fault and normal working conditions were collected. Then, the data were preprocessed and time-domain features were extracted. Finally, the IVY-optimized random forest model was used to identify, diagnose, and classify faults. The simulation results show that the IVY-RF method could identify faulty and normal states with 100% accuracy and distinguish 12 types with 100% accuracy. Compared to seven different algorithms, the IVY-RF improved accuracy by at least 0.17% and up to 67.45% on the original dataset and by at least 1.19% and up to 49.40% in a dataset with 5% noise added. The IVY-RF-based fault diagnosis method shows excellent accuracy and robustness in complex marine environments, providing a reliable fault identification solution for ship power systems. Full article

This work presents an islanded microgrid energy system that uses backstepping control applied to neutral point clamped (NPC) multilevel converters coupled with batteries to behave as virtual generators, able to absorb surplus renewable energy, therefore increasing the penetration of renewable energy sources. Additionally, on a charged battery the virtual generator allows turning-off the backup diesel generator set (GenSet). Aside from improving energy efficiency, the battery-connected multilevel converter aims to regulate frequency, improves power quality, and keeps the microgrid operational in the event of a GenSet failure. The backstepping controlled NPC multilevel converter emulates a virtual generator injecting power to perform as the primary and secondary microgrid frequency controller. Additionally, AC voltage control is implemented, which enables running the islanded microgrid only with multilevel converters, supplied by the battery while integrating solar and wind energy sources. Energy demand and renewable energy forecasts are used to manage the battery state-of-charge. Simulation results, obtained from switched and phasor models show that energy storage and the backstepping frequency control enables the compensation of power fluctuations from renewable energy sources. Furthermore, in the event of the main GenSet failure, the controlled virtual generator keeps the microgrid running for a few minutes, until another GenSet is ready to supply the microgrid. Therefore, the microgrid integration of the battery-connected multilevel converter results in a significant boost in energy efficiency by allowing the disconnection of the backup GenSet. Full article

In the complex environment of microgrid deployments targeted at geographic regions, the seamless integration of renewable energy sources meets a variety of essential challenges. These include the unpredictable nature of renewable energy, characterized by intermittent energy generation, as well as ongoing fluctuations in load demand, the vulnerabilities present in distribution network failures, and the unpredictability that results from unfavorable weather conditions. These unexpected events work together to disturb the delicate balance between energy supply and demand, raising the alarming threat of system instability and, in the worst cases, the sudden advent of damaging blackouts. To address this issue, a fuzzy logic-based energy management system has been developed to monitor, manage, and optimize energy consumption in microgrids. This study focuses on the control of diesel generators and utility grids in a grid-connected microgrid which manages and evaluates numerous energy consumption and distribution features within a specified system, e.g., building or a microgrid. An energy management system is suggested based on fuzzy logic as a swift fix for complications with effective and competent resource management, and its presentation is compared with both the grid-connected and off-grid modes of the microgrid. In the end, the results exhibit that the proposed controller outclasses the predictable controllers in dropping sudden variations that arise during the addition of sources of renewable energy, supporting the refurbishment of the constant system. Full article

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. Full article

During the operation of any source of electrical energy, thermal energy is also generated. The heating of generator parts is accompanied by the loss of the efficiency of the entire system as a whole and eventually leads to failure. In order to remove the heat load from generators based on renewable energy, such as wind turbines and solar panels, it is possible to use heat pumps based on various refrigerants. This article presents a comparative analysis of methods for evaluating the efficiency of the technological process, using the example of increasing the efficiency of the heat pump based on the heat produced by renewable energy installations. An example of improving the efficiency of a laboratory stand is used. Exergetic calculation, fluid selection, an analysis of external sources and emission reduction were performed. This thermal energy transmission system uses a solar panel as an additional low-potential source of heat. Options for increasing the energy efficiency of the installation are considered. An assessment of the reduction in emissions when using an equivalent diesel power plant was carried out using the developed mathematical model. Full article

The safe operation of marine diesel engines (MDEs) is an important safeguard for ships and engine crews at sea. In this paper, a combined neural network prediction model (PCA-CNN-BiLSTM) is proposed for the problem of multi-parameter prediction and fault warning for MDEs. PCA is able to reduce the data dimensions and diminish the redundant information in the data, which helps to improve the training efficiency and generalization ability of the model. CNN can effectively extract spatial features from data, assisting in capturing local patterns and regularities in signals. BiLSTM works to process time series data and capture the temporal dependence in the data, enabling prediction of the failure conditions of MDE, condition monitoring, and prediction of a wide range of thermal parameters with more accuracy. We propose a standardized Euclidean distance-based diesel engine fault warning threshold setting method for ships combined with the standard deviation index threshold to set the diesel engine fault warning threshold. Combined with experimental verification, the method can achieve real-time monitoring of diesel engine operating condition and abnormal condition warning and realize diesel engine health condition assessment and rapid fault detection function. Full article

In recent years, advanced methods and smart solutions have been investigated for the safe, secure, and environmentally friendly operation of ships. Since data acquisition capabilities have improved, data processing has become of great importance for ship operators. In this study, we introduce a novel approach to ship machinery monitoring, employing generative adversarial networks (GANs) augmented with failure mode and effect analysis (FMEA), to address a spectrum of failure modes in diesel generators. GANs are emerging unsupervised deep learning models known for their ability to generate realistic samples that are used to amplify a number of failures within training datasets. Our model specifically targets critical failure modes, such as mechanical wear and tear on turbochargers and fuel injection system failures, which can have environmental effects, providing a comprehensive framework for anomaly detection. By integrating FMEA into our GAN model, we do not stop at detecting these failures; we also enable timely interventions and improvements in operational efficiency in the maritime industry. This methodology not only boosts the reliability of diesel generators, but also sets a precedent for prescriptive maintenance approaches in the maritime industry. The model was demonstrated with real-time data, including 33 features, gathered from a diesel generator installed on a 310,000 DWT oil tanker. The developed algorithm provides high-accuracy results, achieving 83.13% accuracy. The final model demonstrates a precision score of 36.91%, a recall score of 83.47%, and an F1 score of 51.18%. The model strikes a balance between precision and recall in order to eliminate operational drift and enables potential early action in identified positive cases. This study contributes to managing operational excellence in tanker ship fleets. Furthermore, this study could be expanded to enhance the current functionalities of engine health management software products. Full article

The phrase “Multi-Power System (MPS)” refers to an application that combines different energy conversion technologies to meet a specific energy need. These integrated power systems are rapidly being lauded as essential for future decarbonized grids to achieve optimum efficiency and cost reduction. The fact that MPSs multiply several sources also multiplies their advantages to be environmentally friendly and increases the possibility of energy autonomy as they do not depend on a single source. Consequently, this increases the reliability and reduces the production costs and the size of the storage system. However, the main disadvantages of such a system are the complexity of its architecture and the difficulty in managing the power level, which leads the system to face many faults and sometimes failure. In this case, a fault-tolerant control (FTC) system can automatically adapt to component malfunctions while maintaining closed-loop system stability to achieve acceptable performance. However, on the way to build efficient FTC, one first needs to study the faults that may occur in the system in order to tolerate them. This review paper presents the faults of the MPS electrical sources used in a hybrid system, including a photovoltaic generator and a diesel generator, plus a lead–acid battery as a storage device. Only the most-encountered faults are treated. Full article

The urgent demand for clean and renewable energy sources has led to the emergence of the microgrid (MG) concept. MGs are small grids connecting various micro-sources, such as diesel, photovoltaic wind, and fuel cells. They operate flexibly, connected to the grid, standalone, and in clusters. In AC MG control, a hierarchical system consists of three levels: primary, secondary, and tertiary. It monitors and ensures MG stability, power quality, and power sharing based on the specifications of governing protocols. Various challenging transient disturbances exist, such as generator tripping, secondary control failure due to communication delay, and drastic load changes. Although several optimal power sharing methods have been invented, they pose complex control requirements and provide limited improvement. Therefore, in this paper, a novel optimized droop control is proposed using a metaheuristic multi-objective evolutionary algorithm called the Centripetal Force-Gravity Search Algorithm (CF-GSA) to improve the droop performance of power sharing, voltage and frequency stability, and power quality. CF-GSA is an improved algorithm designed to address the issue of local solutions commonly encountered in optimization algorithms. The effectiveness and superiority of the proposed method are validated through a series of simulations. The results of these simulations show that the proposed multi-objective optimization droop control method works well to fix problems caused by power sharing errors in isolated AC microgrids that have to deal with high inductive loads and changes in line impedance. Full article

Improving the productivity and reliability of mining infrastructure is an important task contributing to the mining performance enhancement of any enterprise. Open-pit dump trucks that move rock masses from the mining site to unloading points are an important part of the infrastructure of coal mines, and they are the main transport unit used in the technological cycle during open-pit mining. The failure of any of the mining truck systems causes unscheduled downtime and leads to significant economic losses, which are associated with the need to immediately restore the working state and lost profits due to decreased site productivity and a disruption of the production cycle. Therefore, minimizing the number and duration of unscheduled repairs is a necessity. The most time-consuming operations are the replacement of the diesel engine, traction generator, and traction motors, which requires additional disassembly of the dump truck equipment; therefore, special reliability requirements are imposed on these units. In this article, a mathematical model intended for processing the statistical data was developed to determine the reliability indicators of the brush collector assembly and the residual life of brushes of electric motors, which, unlike existing models, allow the determination of the refined life of the brushes based on the limiting height of their wear. A method to predict the residual life of an electric brush of a DC electric motor is presented, containing a list of controlled reliability indicators that are part of the mathematical model. Using the proposed mathematical model, the reliability of the brush-collector assembly, the minimum height of the brush during operation, and the average rate of its wear were studied and calculated. Full article

Climate-change-related events are increasing the costs of power outages, including losses of product, revenue, and productivity. Given the increase in meteorological disasters in recent years related to climate change effects, the number of costly blackouts, from an economic perspective, has increased in a directly proportional manner. As a result, there is increasing interest in the use of alternators to supply dependable, instantaneous, and uninterruptible electricity. Traditional research has focused on the installation of diesel backup systems to ensure power requirements without deeply considering the resilience capabilities of systems, which is the ability of a system to recover or survive adversity, such as a power outage. This research presents a novel approach focusing on the resiliency impact of backup systems’ storage-free dispatchable solutions on buildings and compares the advantages and disadvantages of biomethane microturbines, natural gas engines, and diesel engines backup systems, discussing the revenue resulting from the resilience provided by emergency generators. The results show that, for several diesel fuel and natural gas safety assumptions, natural gas alternators have a lower probability of failure at the time of a blackout than diesel generators, and therefore, resilience increases. Full article