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To achieve the failure warning of marine systems and their equipment (MSAE), the threshold is one of the most prominent issues that should be solved first. In this study, a fusion model based on sparse Bayes and probabilistic statistical methods is applied to determine a new and more accurate adaptive alarm threshold. A multistep relevance vector machine (RVM) model is established to realize the parameter reconstruction in which the internal uncertainties caused by the degradation process and the external uncertainty caused by the loading, environment, and disturbances were considered. Then, a varying moving window (VMW) method is employed to determine the window size and achieve continuous data reconstruction. Further, the model based on Johnson distribution systems is utilized to complete the transformation of the residual parameters and calculate the adaptive threshold. Finally, the proposed adaptive decision threshold is successfully involved in the actual examples of the peak pressure and exhaust temperature of marine diesel engines. The results show that the proposed method can realize the continuous health condition monitoring of MSAE, successfully detect abnormal conditions in advance, achieve an early warning of failure, and reserve sufficient time for decision-making to prevent the occurrence of catastrophic disasters.

ObjectiveA ship's system is a complex mechanism composed of multiple pieces of equipment. Due to the dynamic and non-linear characteristics of the parameters of each component, fault diagnosis is complicated. This paper proposes a dynamic feature fusion method for performing efficient fault diagnosis on the system.MethodsFractal theory, dynamic theory and the kernel principal component analysis (KPCA) method are used to reconstruct, map and filter the system state data, and obtain the principal component characteristic data matrix, square prediction error (SPE) and corresponding control limits. An offline monitoring model based on the health data of a marine diesel engine intake and exhaust system is then constructed and used to diagnose and analyze ship system faults. In order to verify the validity of the model, the fault data of the intake and exhaust system of a marine diesel engine is selected for verification and analysis. ResultsThe results show that this method can effectively realize the accurate analysis of a system's dynamic nonlinear state data and efficient analysis and diagnosis of faults, with better fault diagnosis performance than the KPCA and support vector machine (SVM) method.ConclusionsThe method proposed in this paper can realize the detection and diagnosis of marine diesel engine intake and exhaust system failures, and improve the reliability and safety of system operation.

Implantable electronic tags are crucial for the conservation of marine biodiversity. However, the power supply associated with these tags remains a significant challenge. In this study, an underwater flexible triboelectric nanogenerator (UF-TENG) was proposed to harvest the biomechanical energy from the movements of marine life, ensuring a consistent power source for the implantable devices. The UF-TENG, which is watertight by the protection of a hydrophobic poly(tetrafluoroethylene) film, consists of high stretchable carbon black-silicone as electrode and silicone as a dielectric material. This innovative design enhances the UF-TENG’s adaptability and biocompatibility with marine organisms. The UF-TENG’s performance was rigorously assessed under various conditions. Experimental data highlight a peak output of 14 V, 0.43 μA and 38 nC, with a peak power of 2.9 μW from only one unit. Notably, its performance exhibited minimal degradation even after three weeks, showing its excellent robustness. Furthermore, the UF-TENG is promising in the self-powered sensing of the environmental parameter and the marine life movement. Finally, a continuous power supply of an underwater temperature is achieved by paralleling UF-TENGs. These findings indicate the broad potential of UF-TENG technology in powering implantable electronic tags.

When high uncertainty becomes the norm in entrepreneurship, entrepreneurial failure often becomes the first natural barrier that new ventures face. In dynamic environment, there is still a lack of clear answers on what strategic orientation new ventures need to adopt to improve organizational learning efficiency and achieve sustainable development. Lean startup theory believes that the entrepreneurial process is also a process where organizational cognition is constantly iterated and updated, which drives entrepreneurs to explore business opportunities through iteration learning and early customer insight. The paper aims to describe and examine the relationship between lean startup strategy, organizational iterative learning, and sustainable development of new ventures. This model is tested on the survey data of R&D departments of 325 technology new ventures in China. The research results show that: lean startup strategy can positively affect sustainable performance of new ventures; organizational iterative learning plays a mediating role in the relationship between lean startup strategy and sustainable development; market dynamics positively moderate the relationship between organizational iterative learning and sustainable development, while technology dynamics negatively moderate this relationship; furthermore, the two also moderate the process of lean startup strategy influencing sustainable development through organizational iterative learning, and that moderated mediating effect is significant. The research results reveal that entrepreneurs should deepen lean startup practices in new business layout, advocate the iteration and output of advanced knowledge, help enterprises establish proprietary knowledge barriers, and achieve sustainable development.

According to statistics, about 70% of ship fire accidents occur in the engine room, due to the complex internal structure and various combustible materials. Once a fire occurs, it is difficult to extinguish and significantly impacts the crew’s life and property. Therefore, it is urgent to design a method to detect the fire phenomenon in the engine room in real time. To address this problem, a machine vision model (CWC-YOLOv5s) is proposed, which can identify early fires through smoke detection methods. Firstly, a coordinate attention mechanism is added to the backbone of the baseline model (YOLOv5s) to enhance the perception of image feature information. The loss function of the baseline model is optimized by wise intersection over union, which speeds up the convergence and improves the effect of model checking. Then, the coordconv coordinate convolution layer replaces the standard convolution layer of the baseline model, which enhances the boundary information and improves the model regression accuracy. Finally, the proposed machine vision model is verified by using the ship video system and the laboratory smoke simulation bench. The results show that the proposed model has a detection precision of 91.8% and a recall rate of 88.1%, which are 2.2% and 4.6% higher than those of the baseline model.

Leak monitoring is essential for the intelligent operation and maintenance of marine systems, and can effectively prevent catastrophic accidents on ships. In response to this challenge, a machine vision-based leak model is proposed in this study and applied to leak detection in different types of marine system in complex engine room environments. Firstly, an image-based leak database is established, and image enhancement and expansion methods are applied to the images. Then, Standard Convolution and Fast Spatial Pyramid Pooling modules are added to the YOLOv5 backbone network to reduce the floating-point operations involved in the leak feature channel fusion process, thereby improving the detection speed. Additionally, Bottleneck Transformer and Shuffle Attention modules are introduced to the backbone and neck networks, respectively, to enhance the feature representation performance, select critical information for the leak detection task, and suppress non-critical information to improve detection accuracy. Finally, the proposed model’s effectiveness is verified using leak images collected by the ship’s video system. The test results demonstrate that the proposed model exhibits excellent recognition performance for various types of leak, especially for drop-type leaks (for which the accuracy reaches 0.97).

Objective In light of problems such as the untimely condition monitoring and alarm, excessively large threshold bandwidth and inaccurate condition evaluation parameters of intelligent ship power system equipment, an adaptive threshold method is proposed to monitor, alarm and evaluate the conditions of such equipment. Method First, a simulated annealing algorithm is used to optimize the support vector regression (SVR) machine prediction model to simulate the general state characteristic parameters of the power system equipment. Then, after the normal transformation of the modeling residual, combined with the sliding time window, the adaptive threshold model is constructed. Finally, the exhaust gas temperature of the ship's main propulsion diesel engine is selected as the research object for example verification. Results The results show that compared with the traditional fixed threshold, the adaptive threshold model has more compact bandwidth and good adaptability, and can identify abnormal phenomena in power system equipment in advance. Conclusion This method improves the efficiency and threshold accuracy of monitoring and alarm systems, and provides an effective means of early fault diagnosis and a more accurate basis for system status evaluation.

Implementation of new emissions regulations calls for a reassessment of the emissions levels of newly built ships sailing in Chinese regions. In this paper, marine diesel engines are subjected to emissions bench tests using high-precision testing equipment. A total of 135 marine diesel engines meeting the Limits and Measurement Methods for Exhaust Pollutants from Marine Engines (CHINA I/II) were first systematically analyzed. The emission factors of marine main engines (ME) and auxiliary engines (AE) were obtained under different displacements. The results show that the fuel-based emission factors for NOX + HC and CO meeting CHINA I/II are 25.80~44.87/16.47~46.35 and 2.47~13.22/1.64~5.62 kg/t-fuel, respectively. The energy-based emission factors for NOX + HC, CO, CO2, and PM satisfying CHINA I/II are 5.70~9.24/3.70~9.07, 0.49~2.30/0.36~0.99, 620~683/612~718, and 0.05~0.36/0.05~0.27 g/kWh, respectively. Additionally, the specific emission of NOx rises with the increase in single-cylinder displacement, so the CO emission limit of pure diesel fuel is recommended to be lower than 5 g/kWh. The results in this paper provide valuable basic data for research on and estimation of ship emissions in waterway transportation and for understanding the emission characteristics of marine diesel engines.

It is well known that sail-assisted propulsion works under gradient wind conditions in the atmospheric boundary layer, and is an energy-saving device for fuel consumption. In order to study the aerodynamic characteristics of a wingsail in the atmospheric boundary layer above sea level, a transition SST turbulence model was used for numerical simulation of the wingsail with uniform and gradient wind conditions. We concluded that gradient wind conditions can delay the stall caused by an increased angle of attack. This is because the airflow on the suction surface of the wingsail in the spanwise direction exerts an acceleration towards the top of the wingsail. At the same time, supplementary airflow compresses the separated vortex, thus delaying the stall of the two-element wingsail. Under gradient wind conditions, the flow separation of the wingsail develops rapidly in the stall angles. Once a flat separation vortex is formed at the trailing edge of the wingsail, with the slow increase of flap deflection angle, flow separation rapidly expands and a deep stall occurs. Therefore, a small change of the flap deflection angle in the near-stall angles may lead to a deep stall of the wingsail, which should be avoided in engineering applications. Finally, the influence of the average speed of the gradient wind on the aerodynamic performance of the two-element wingsail was analyzed.

Exhaust gas flow takes a vital position in the assessment of ship exhaust emissions, and it is essential to develop a self-powered and robust exhaust gas flow sensor in such a harsh working environment. In this work, a bearing type triboelectric nanogenerator (B-TENG) for exhaust gas flow sensing is proposed. The rolling of the steel balls on PTFE film leads to an alternative current generated, which realizes self-powered gas flow sensing. The influence of ball materials and numbers is systematically studied, and the B-TENG with six steel balls is confirmed according to the test result. After design optimization, it is successfully applied to monitor the gas flow with the linear correlation coefficient higher than 0.998 and high output voltage from 25 to 106 V within the gas flow of 2.5–14 m/s. Further, the output voltage keeps stable at 70 V under particulate matter concentration of 50–120 mg/m3. And the output performance of the B-TENG after heating at 180 °C for 10 min is also surveyed. Moreover, the mean error of the gas flow velocity by the B-TENG and a commercial gas flow sensor is about 0.73%. The test result shows its robustness and promising perspective in exhaust gas flow sensing. Therefore, the present B-TENG has a great potential to apply for self-powered and robust exhaust gas flow monitoring towards Green Ship.

With the rapid development of advanced electronics/materials and manufacturing, marine vibration sensors have made great progress in the field of ship and ocean engineering, which could cater to the development trend of marine Internet of Things (IoT) and smart shipping. However, the use of conventional power supply models requires periodic recharging or replacement of batteries due to limited battery life, which greatly causes too much inconvenience and maintenance consumption, and may also pose a potential risk to the marine environment. By using the coupling effect of contact electrification and electrostatic induction, triboelectric nanogenerators (TENGs) were demonstrated to efficiently convert mechanical vibration movements into electrical signals for sensing the vibration amplitude, direction, frequency, velocity, and acceleration. In this article, according to the two working modes of harmonic vibration and non-harmonic vibration, the latest representative achievements of TENG-based vibration sensors for sensing mechanical vibration signals are comprehensively reviewed. This review not only covers the fundamental working mechanism, rational structural design, and analysis of practical application scenarios, but also investigates the characteristics of harmonic vibration and non-harmonic vibration. Finally, perspectives and challenges regarding TENG-based marine self-powered vibration sensors at present are discussed.

Pervasive and continuous energy solutions are highly desired in the era of the Internet of Things for powering wide-range distributed devices/sensors. Wind energy has been widely regarded as an ideal energy source for distributed devices/sensors due to the advantages of being sustainable and renewable. Herein, we propose a high-performance flag-type triboelectric nanogenerator (HF-TENG) to efficiently harvest widely distributed and highly available wind energy. The HF-TENG is composed of one piece of polytetrafluoroethylene (PTFE) membrane and two carbon-coated polyethylene terephthalate (PET) membranes with their edges sealed up. Two ingenious internal-structure designs significantly improve the output performance. One is to place the supporting sponge strips between the PTFE and the carbon electrodes, and the other is to divide the PTFE into multiple pieces to obtain a multi-degree of freedom. Both methods can improve the degree of contact and separation between the two triboelectric materials while working. When the pair number of supporting sponge strips is two and the degree of freedom is five, the maximum voltage and current of HF-TENG can reach 78 V and 7.5 μA, respectively, which are both four times that of the untreated flag-type TENG. Additionally, the HF-TENG was demonstrated to power the LEDs, capacitors, and temperature sensors. The reported HF-TENG significantly promotes the utilization of the ambient wind energy and sheds some light on providing a pervasive and sustainable energy solution to the distributed devices/sensors in the era of the Internet of Things.

Vibration is a common phenomenon in various fields which can not only indicate the working condition of the installation, but also serve as an energy source if it is efficiently harvested. In this work, a robust silicone rubber strip-based triboelectric nanogenerator (SRS-TENG) for vibration energy harvesting and multi-functional self-powered sensing is proposed and systematically investigated. The SRS-TENG consists of a silicone rubber strip and two aluminum electrode layers supported by polylactic acid (PLA), and acts as a sustainable power source and vibration frequency, amplitude and acceleration sensor as well. The soft contact between the aluminum electrode and silicone rubber strip makes it robust and stable even after 14 days. It can be applied in ranges of vibration frequencies from 5 to 90 Hz, and amplitudes from 0.5 to 9 mm, which shows it has advantages in broadband vibration. Additionally, it can achieve lower startup limits due to its soft structure and being able to work in multi-mode. The output power density of the SRS-TENG can reach 94.95 W/m3, matching a resistance of 250 MΩ, and it can light up more than 100 LEDs and power a commercial temperature sensor after charging capacitors. In addition, the vibration amplitude can be successfully detected and displayed on a human–machine interface. Moreover, the frequency beyond a specific limit can be distinguished by the SRS-TENG as well. Therefore, the SRS-TENG can be utilized as an in situ power source for distributed sensor nodes and a multifunctional self-powered vibration sensor in many scenarios.

In recent years, sensors have been moving towards the era of intelligence, miniaturization and low power consumption, but the power-supply problem has always been a key issue restricting the popularization and development of machine-mounted sensors on the rotating machinery. Herein, we develop a ring-type triboelectric nanogenerator (R-TENG) that functions as a sustainable power source as well as a self-powered rotational speed sensor for rotating machinery. The R-TENG adopts a freestanding mode and consists of a ring-type container unit, an end cover and polytetrafluoroethylene (PTFE) cylinders. In this study, the influence of the number of cylinders, the PTFE cylinder’s diameter and the rotational speed on the electrical output are systematically examined, and the motion law of the PTFE cylinders in the container is revealed by the experimental results and verified by kinetic simulation. At a rotational speed of 400 rpm, the output voltage, current and transferred charge of the designed R-TENG reached 138 V, 115 nC and 2.03 μA, respectively. This study provides an attractive power supply strategy for machine-mounted sensors of the rotating machinery, and the rotational speed measurement test also suggests the potential application of the R-TENG as a self-powered rotational speed sensor.

Ship mechanical system health prognosis is one of the major tasks of ship intelligent operation and maintenance (O&M). However, current failure prediction methods are aimed at single pieces of equipment, and system-level monitoring remains an underexplored area. To address this issue, an integration method based on a synthesized health indicator (SHI) and dynamic hybrid prediction is proposed. To accurately reflect the changes in system health conditions, a multi-state parameter fusion method based on dynamic kernel principal component analysis (DKPCA) and the stacked autoencoder (SAE) is presented, along with construction of a system SHI. Taking into consideration that the system degradation process includes global degradation trends, local self-healing phenomena, and local interference, a dynamic hybrid prediction model is established after SHI decomposition. The performance of the proposed approach is applied to a ship fuel-oil system to show its effectiveness.

Prognostics and health management (PHM) is an essential means to optimize resource allocation and improve the intelligent operation and maintenance (O&M) efficiency of marine systems and equipment (MSAE). PHM generally consists of four technical processes, namely health condition motoring (HCM), fault diagnosis (FD), health prognosis (HP), and maintenance decision (MD). In recent years, a large amount of research has been implemented in each process. However, there is not any systematic review that covers the technical framework comprehensively. This article presents a review of the framework of PHM in the marine field to fill the gap. First, the essential HCM methods, which are widely observed in the academic literature, are introduced systematically. Then, the commonly used FD approaches and their applications in MSAE are summarized, and the implementation process of intelligent methods is systematically introduced. After that, the technologies of HP have been reviewed, including the construction of health indicator (HI), health stage (HS) division, and popular remaining useful life (RUL) prediction approaches. Afterwards, the evolution of maintenance strategy in the maritime field is reviewed. Finally, the challenges of implementing PHM for intelligent ships are put forward.

In order to accurately assess China’s port air pollution caused by the shipping industry, two main methods can be used to calculate the emissions of ships, including the method based on ship fuel consumption and the method based on ship activities. Both methods require accurate diesel engine emission factors, or specific emissions. In this paper, the emission characteristics of NOX, CO, CO2 and THC from 197 domestic marine diesel engines were tested under bench test conditions by a standard emission measurement system. The diesel engines were divided into six Classes, A~F, according to their power distribution, and the fuel-based emission factors and energy-based emission factors of marine main engine and auxiliary engine meeting IMO NOX Tier II standards were given. The results showed that the main engine fuel-based emission factors of NOX, CO, CO2 and THC from Class A to Class F were 33.25~76.58, 2.70~4.33, 3123.92~3166.47 and 1.10~2.64 kg/t-fuel, respectively; and the energy-based emission factors were 6.57~11.75, 0.56~0.81, 530.28~659.71 and 0.18~0.61 g/kW h, respectively. The auxiliary engine fuel-based emission factors of NOX, CO, CO2 and THC from Class A to Class D were 27.17~39.81, 2.66~5.12, 3113.01~3141.34 and 1.16~2.87 kg/t-fuel respectively; and their energy-based emission factors were 6.06~8.33, 0.47~0.77, 656.86~684.91 and 0.21~0.61 g/kW h, respectively. The emission factors for different types of diesel engines were closely related to the diesel engine load, and the relation between them could be expressed by quadratic polynomial or power function. The results of this paper provide valuable data for the estimation of waterway transportation exhaust emissions and comprehensive understanding of the emission characteristics of marine diesel engines.

With the development of intelligent ship, types of advanced sensors are in great demand for monitoring the work conditions of ship machinery. In the present work, a self-powered and highly accurate vibration sensor based on bouncing-ball triboelectric nanogenerator (BB-TENG) is proposed and investigated. The BB-TENG sensor consists of two copper electrode layers and one 3D-printed frame filled with polytetrafluoroethylene (PTFE) balls. When the sensor is installed on a vibration exciter, the PTFE balls will continuously bounce between the two electrodes, generating a periodically fluctuating electrical signals whose frequency can be easily measured through fast Fourier transform. Experiments have demonstrated that the BB-TENG sensor has a high signal-to-noise ratio of 34.5 dB with mean error less than 0.05% at the vibration frequency of 10 Hz to 50 Hz which covers the most vibration range of the machinery on ship. In addition, the BB-TENG can power 30 LEDs and a temperature sensor by converting vibration energy into electricity. Therefore, the BB-TENG sensor can be utilized as a self-powered and highly accurate vibration sensor for condition monitoring of intelligent ship machinery.

The rigid wingsail is a new type of propulsion equipment which greatly improves the performance of the sailboat under the conditions of upwind and downwind. However, such sail-assisted devices are not common in large ships because the multi-element wingsail is sensitive to changes in upstream flow, making them difficult to operate. This problem shows the need for aerodynamic study of wingsails. A model of two-element wingsail is established and simulated by the steady and unsteady RANS approach with the k-ω SST turbulence model and compared with the known experimental data to ensure the accuracy of the numerical simulation. Then, some key design and structural parameters (camber, the rotating axis position of the flap, angle of attack, flap thickness) are used to characterize the aerodynamic characteristics of the wingsail. The results show that the position of the rotating shaft of the flap has little influence on the lift coefficient at low camber. When stall occurs, the lift coefficient first increases and then decreases as the flap axis moves backward, which also delays the stall angle at a low camber. At the high camber of AOA = 6°, the lift coefficient always increases with the increase of the rotating axis position of the flap; especially between 85% and 95%, the lift coefficient increases suddenly, which is caused by the disappearance of large-scale flow separation on the suction surface of the flap. It reflects the nonlinear coupling effect between camber of wingsail and the rotating axis position of the flap

The methane (CH4) burning interruption factor and the characteristic values characterizing the flame combustion state in the engine cylinder were defined. The logical mapping relationship between image feature values and combustion conditions in the framework of iconology was proposed. Results show that there are two periods of combustion instability and combustion stability during the combustion of dual fuel. The high temperature region with a cylinder temperature greater than 1800K is the largest at 17°CA after top dead center (TDC), accounting for 73.25% of the combustion chamber area. During the flame propagation, the radial flame velocity and the axial flame velocity are “unimodal” and “wavy,” respectively. During the combustion process, the CH4 burning interruption factor first increased and then decreased. The combustion duration in dual fuel mode is 21.25°CA, which is 15.5°CA shorter than the combustion duration in pure diesel mode. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Engineering Research (2307-1877) is the property of Kuwait University, Academic Publication Council and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)