Search Results (26)

With the continuous advancement of intelligent, integrated, and sophisticated modern marine equipment, bearing fault diagnosis faces increasingly severe technical challenges. Compared with traditional industrial environments, marine propulsion systems are characterized by multi-bearing coupled vibrations and complex operating conditions. To address these characteristics, this paper proposes a fault diagnosis method that combines a least squares support vector machine (LSSVM) with multi-domain feature extraction based on an improved hippopotamus optimization algorithm (LCM-HO). This method directly extracts time, spectral, and time-frequency domain features from the raw signal, effectively avoiding complex preprocessing and enhancing its potential for field engineering applications. Experimental verification using the Paderborn bearing dataset and a self-built marine bearing test bench demonstrates that the LCM-HO-LSSVM method achieves diagnostic accuracy rates of 99.11% and 98.00%, respectively, demonstrating significant performance improvements. This research provides a reliable, efficient, and robust technical solution for bearing fault diagnosis in complex marine environments. Full article

Ships play a crucial role in modern society, serving purposes such as marine transportation, tourism, and exploration. Malfunctions or defects in the main engine, which is a core component of ship operations, can disrupt normal functionality and result in substantial financial losses. Consequently, early fault diagnosis of abnormal engine conditions is critical for effective maintenance. In this paper, we propose a deep hybrid model for fault diagnosis of ship main engines, utilizing exhaust gas temperature data. The proposed model utilizes both time-domain features (TDFs) and time-series raw data. In order to effectively extract features from each type of data, two distinct feature extraction networks and an attention module-based classifier are designed. The model performance is evaluated using real-world cylinder exhaust gas temperature data collected from the large ship low-speed two-stroke main engine. The experimental results demonstrate that the proposed method outperforms conventional methods in fault diagnosis accuracy. The experimental results demonstrate that the proposed method improves fault diagnosis accuracy by 6.146% compared to the best conventional method. Furthermore, the proposed method maintains superior performanceeven in noisy environments under realistic industrial conditions. This study demonstrates the potential of using exhaust gas temperature using a single sensor signal for data-driven fault detection and provides a scalable foundation for future multi-sensor diagnostic systems. Full article

A novel anomaly detection algorithm is presented to analyze a group of signals that must be correlated under normal conditions. The method is called Clustered Correlation Health Scan (CCH-Scan). It detects abnormal signals, the durations corresponding to abnormalities, and the degree of abnormality. This algorithm is applied to a case study on fault diagnosis in the cylinders of a 12-cylinder marine dual-fuel engine. In particular, 12 Exhaust Valve Closing Dead Time (ECDT) signals are analyzed to detect abnormalities. Although these signals are critical and any abnormality in them requires urgent intervention, this is the first time they have been discussed in the literature. The details of the algorithm are elaborated, its parameters are studied, and the effects of these parameters on the results are measured and analyzed using a quality score. In addition, a metric to measure the degree of abnormality of the signal is introduced. The method detects abnormal signals, the durations of abnormalities, and the degrees of abnormalities. The results align with ground-truth data from an available technical industrial maintenance report. The approach demonstrates promising potential for application in various other contexts. Full article

The condition monitoring of mooring equipment is an important engineering reliability issue during the operation of a floating production storage and offloading unit (FPSO). The chain jack (CJ) is the key equipment for powering the mooring chain in a spread mooring system. Under complex and dynamic marine operating conditions, different severity faults in the CJ hydraulic system display distinct time-scale characteristics. Hence, this paper proposes a real-time fault diagnosis method of the CJ hydraulic system based on multi-scale feature fusion. Firstly, the model incorporates a convolutional neural network (CNN) layer to extract localized spatial features from multivariate time-series data, effectively identifying fault patterns over the associated short intervals. Subsequently, the bidirectional long short-term memory (BiLSTM) layer is introduced to construct a dynamic temporal model to comprehensively capture the evolution of the fault severity. Finally, a multi-scale global attention mechanism (GAM) emphasizes persistent fault behaviors across time scales, dynamically prioritizing relevant features to improve diagnostic accuracy and model interpretability. The study results indicate that the proposed model’s accuracy improves by 7.36% over the CNN-GAM for 11 failure modes, up to 99.34%. This study contributes to the safe operation of an FPSO by guiding monitoring CJ operations under different load conditions. Full article

The implementation of a system for the acquisition, transferring and processing of data essential for marine engine diagnostics is the basis of condition maintenance. Determining the most influential operating parameters, and conducting monitoring, analysis and taking action based on expert knowledge prevents downtime due to possible malfunctions. Timely corrections and replacements of worn parts based on condition diagnostics enable maintenance planning, which reduces the frequency of maintenance and the accumulation of unnecessary spare parts in warehouses. For research purposes, a system for remote data collection from electronically controlled marine engines was developed and applied. The system was installed on a four-stroke high-speed propulsion engine from a ferry, and the operating parameters of the engine were monitored during regular sailing in order to detect irregularities and possible failures at an early stage. The measurement system monitored the parameters obtained through the electronic engine control module via the J1939 protocol, and in this paper, the following relevant engine parameters were analyzed: engine speed, boost pressure, fuel consumption and engine load at the current speed. The analysis included the creation of trend diagrams to present the distribution of the minimum, median and maximum values of each parameter of all the measurements performed. This study also examined the simulation of the faults of the high-speed four-stroke marine engine model. By utilizing sensor data from critical system components, this research investigated different scenarios. The analysis aimed to elucidate the impact of these faults on engine performance. Based on the analyses of the relevant operating parameters of the engine, diagnostics were carried out. Full article

In response to the typical fault issues encountered during the operation of marine diesel engines, a fault diagnosis method based on a convolutional neural network (CNN), a temporal convolutional network (TCN), and the attention mechanism (ATTENTION) is proposed, referred to as CNN-TCN–ATTENTION. This method successfully addresses the issue of insufficient feature extraction in previous fault diagnosis algorithms. The CNN is employed to capture the local features of diesel engine faults; the TCN is employed to explore the correlations and temporal dependencies in sequential data, further obtaining global features; and the attention mechanism is introduced to assign different weights to the features, ultimately achieving intelligent fault diagnosis for marine diesel engines. The results of the experiments demonstrate that the CNN-TCN–ATTENTION-based model achieves an accuracy of 100%, showing superior performance compared to the individual CNN, TCN, and CNN-TCN methods. Compared with commonly used algorithms such as Transformer, long short-term memory (LSTM), Gated Recurrent Unit (GRU), and Deep Belief Network (DBN), the proposed method demonstrates significantly higher accuracy. Furthermore, the model maintains an accuracy of over 90% in noise environments such as random noise, Gaussian noise, and salt-and-pepper noise, demonstrating strong diagnostic performance, generalization capability, and noise robustness. This provides a theoretical basis for its practical application in the fault diagnosis of marine diesel engines. 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

In modern intelligent shipping, ensuring the stable and reliable technical condition of marine diesel engines is critical for safe and efficient vessel operations. Conventional fault diagnosis approaches and many existing Transformer-based methods often focus on single-scale features, potentially overlooking subtle fault indicators and reducing diagnostic accuracy under complex working conditions. To address these limitations, this paper proposes a Multi-Scale Attention Transformer (MSAT) model that integrates both high- and low-resolution attention mechanisms. This multi-scale strategy enhances the extraction of detailed and coarse-grained features, improving the model’s capacity to detect and characterize complex diesel engine faults. Additionally, an optimized Nadam optimizer is employed to refine convergence speed and accuracy, surpassing the Adam-based baseline by 0.71%. Rigorous testing on a publicly available diesel engine fault dataset demonstrates that the MSAT model achieves a diagnostic accuracy of 99.86% at a 60 dB signal-to-noise ratio (SNR), outperforming established models such as GRU and LSTM by more than 1%. Even under severe noise interference (0 dB SNR), the model maintains a high accuracy of 96.86%, highlighting its robustness and suitability for real-time monitoring in challenging marine environments. By quantitatively validating these improvements in diagnostic accuracy and noise resistance, this work offers a novel and effective solution for predictive maintenance and operational condition assessment of marine diesel engines, contributing to the reliability and safety of intelligent shipping systems. Full article

Vietnam’s seaport system currently includes 298 ports with 588 wharves (a total length of approximately 92,275 m), which is vital in developing Vietnam’s marine economy. The piled pier, a type of wharf structure, is widely used and accounts for up to 90%, while the remaining 10% is made up of other types of wharf structures, such as gravity and sheet pile quay walls. Most wharves have been operating for over 10 years and some for even more than 50 years. Noticeably, wharves are highly vulnerable and degrade rapidly due to many factors, especially heavy load impacts and severe environmental conditions. Additionally, wharves have a higher risk of deterioration than other inland infrastructure, such as buildings and bridges. Consequently, determining a wharf’s health is an important task in maintaining normal working conditions, extending its lifecycle, and avoiding other severe damage that could lead to dangers to the safety of vehicles, facilities, and humans. Moreover, regulated quality inspections usually include only simple inspections, e.g., displacement, settlement, geometric height, and tilt; the visual inspection and determination of dimensions by simple length-measuring equipment; concrete strength testing by ultrasonic and rebound hammers; and the experimental identification of the chloride ion concentration, chloride diffusion coefficient, corrosion activity of rebar in concrete, and steel thickness. These testing methods often give local results depending on the number of test samples. Therefore, advanced diagnostic techniques for assessing the technical condition of piled piers need to be studied. The impact vibration test (IVT) is a powerful non-destructive evaluation method that indicates the overall health of structures, e.g., underground and foundation structures, according to official standards. Hence, the IVT is expected to help engineers detect the potential deterioration of overall structures. It is fundamental that, if a structure is degraded, its natural frequency will be affected. A structure’s health index and technical condition are determined based on this change. However, the IVT does not seem to be widely applied to piled piers, with no published standard; hence, controversial issues related to accuracy and reliability still remain. This motivates the present study to recommend an adjusted factor (equal to 1.16) for the health index (classified in official standards for other structures) through numerical and experimental approaches before officially applying the IVT method to piled piers. The current work focuses on the health index using the design natural frequency, which is more practical in common cases where previous historical data and the standard natural frequency are unavailable. This study also examines a huge number of influencing factors and situations through theoretical analysis, experience, and field experiments to propose an adjusted indicator. The results are achieved with several assumptions of damages, such as the degradation of materials and local damages to structural components. With the proposed adjusted indicator, the overall health of piled piers can be assessed quickly and accurately by IVT inspections in cases of incidents, accidents due to collisions, cargo falls during loading and unloading, or subsidence and erosion due to natural disasters, storms, and floods. Full article

Damage localization in GFRP (glass-fiber-reinforced polymer) composite plates is a crucial research area in marine engineering. This study introduces a feedback-based damage index (DI) combined with multi-label classification to enhance the accuracy of damage localization and address scenarios involving multiple damages. The research begins with the creation of a modal database for yachts’ GFRP composite plates using finite element modeling (FEM). A method for deriving a feedback-weighted matrix, based on the accuracy of the DI, is then developed. Sensitivity analysis reveals that the feedback DI is 50% more sensitive than the traditional DI, reducing false positives and missed detections. The associated feedback-weighted matrix depends solely on the structural shape, ensuring its transferability. To address the challenge for localizing multiple damages, a multi-label classification approach is proposed. The synergy between the feedback optimization and multi-label classification enables the rapid and precise localization of multiple damages in GFRP composite plates. Modal testing on damaged GFRP plates confirms the enhanced accuracy for combining the feedback DI with multi-label classification for pinpointing damage locations. Compared with traditional methods, this feedback DI method improves sensitivity, while multi-label classification effectively handles multiple damage scenarios, enhancing the overall efficiency of the damage diagnosis. The effectiveness of the proposed methods is validated through experimentation, offering robust theoretical support for composite plate damage diagnostics. Full article

Marine engines confront challenges of varying working conditions and intricate failures. Existing studies have primarily concentrated on fault diagnosis in a single condition, overlooking the adaptability of these methods in diverse working condition. To address the aforementioned issues, we propose a cross working condition fault diagnosis method named the Balanced Adaptation Domain Weighted Adversarial Network (BADWAN). This method combines transfer learning to tackle the challenges of cross working condition diagnosis with limited labels. Specifically tailored for scenarios with incomplete labeling in the target working conditions, we designed an Enhanced Centroid Balance scheme to balance the label space, thereby enhancing the model’s transfer capabilities. Additionally, we designed an Instance Affinity Weighting scheme on the foundation of Class-level Weighting, refining the model to the instance level for effective information interaction. Furthermore, we incorporated the Adaptive Uncertainty Suppression strategy to further boost the model’s classification prowess. Two experimental scenarios were designed to demonstrate the effectiveness of the proposed model using a Wärtsilä9L34DF dual-fuel engine as an experimental subject. The results demonstrate an over 90% diagnostic accuracy in scenarios with complete target working condition labels and 86% accuracy in scenarios with incomplete labels, outperforming other transfer learning models. The BADWAN model excels in cross-condition fault diagnosis tasks for marine engines with incomplete target working condition labels, offering a novel solution to this field. Full article

This article deals with the problems related to the difficulties in the vibration diagnostics of modern marine engines. The focus was on the injection system, with a particular emphasis on injectors. An unusual approach to the implementation of research enabling the smooth regulation of the opening pressure of the mechanical injector during engine operation at a constant load was presented. This approach obtained repeatability of conditions for subsequent measurements, which is very difficult to achieve when using the classic approach that forces the injector to be disassembled after each test. Full article

The article presents the possibility of using machine learning (ML) in artificial intelligence to classify the technical state of marine engine injectors. The technical condition of the internal combustion engine and injection apparatus significantly determines the composition of the outlet gases. For this purpose, an analytical package using modern technology assigns experimental test scores to appropriate classes. The graded changes in the value of diagnostic parameters were measured on the injection subsystem bench outside the engine. The influence of the operating conditions of the fuel injection subsystem and injector condition features on the injector needle vibration displacement waveforms was subjected to a neural network (NN) ML process and then tested. Diagnostic parameters analyzed in the amplitude, frequency, and time–frequency domains were subjected after a learning process to recognize simulated various regulatory and technical states of suitability and unfitness with single and complex damage of new and worn injector nozzles. Classification results were satisfactory in testing single damage and multiple changes in technical state characteristics for unfitness states with random wear injectors. Testing quality reached above 90% using selected NNs of Statistica 13.3 and MATLAB R2022a environments. Full article

In this article, we discuss the importance of the analysis of indicator diagrams and indicated parameters in operational diagnostics of marine engines. An innovative method was devised to improve the effectiveness of diagnostics based on this information. It consisted of the elimination of harmful measurement spaces during cylinder pressure measurements as well as an in-depth analysis of the resultant indicator diagrams based on the functions of heat release. This research demonstrated a negative impact on the quality of indicator diagrams and the values of the parameters indicated by cylinder pressure measurements with sensors mounted on indicator cocks. The elimination of the indicator cock and measuring channel in the cylinder pressure measurements affected the quality of the indicator diagrams and, based on the calculated heat-release functions, allowed the emergence of new (additional) diagnostic symptoms. This could significantly improve the effectiveness of diagnostics performed in operating conditions and, as a result, the effective, trouble-free, and ecological operation of marine engines, thus meeting the growing environmental and operational safety requirements. Full article

In the operation of internal combustion engines, despite technical state monitoring, some cracks that develop in metal components go undetected, leading to secondary, critical, or degradation damage. The diagnostic systems used in floating objects mainly use quasi-static thermodynamic signals, which alert operators too late about emerging damage. Although various methods have been developed to detect cracks in internal combustion engine components, the effectiveness and implementation of the proposed methods are not satisfactory. Therefore, this article presents the use of selected vibration and in-cylinder pressure signals to diagnose the development of damage in some components of marine diesel engines. The investigations were conducted under the natural conditions of the operation of sea-going vessels during port-handling operations. During these investigations, it was possible to observe clear changes in the values of diagnostic symptoms, which corresponded to the development of damage. The developing damage detected in the study involved cracks in injector nozzles manufactured from alloy steel. Despite advances in design, materials, and manufacturing technology, injector nozzle cracks still occur. The diagnostic symptoms used to detect damage development were the amplitude and spectral and wavelet measurements of vibration acceleration signals. This work aimed to search for crack-oriented methods of signal analysis, for example, computer visualization and the recording of diagnostic parameters in various domains. Decimation, windowed, time, amplitude, and time-frequency domain analyses; wavelet statistics; color analysis; and machine learning were used for classification using artificial neural networks. Experimental investigations showed the possibility of diagnosing the development processes of damage to marine diesel engines. The advanced signal processing methods used made it possible to obtain many signal measurements, from which the most useful diagnostic symptoms were selected. The new symptoms found with decimation, time-domain windowed analysis, and Haar wavelet statistics were more useful than the existing ones. Full article