Search Results (14)

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. Full article

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. Full article

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. Full article

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). Full article

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 NO_X + 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 NO_X + 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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

Harvesting wind energy from the ambient environment is a feasible method for powering wireless sensors and wireless transmission equipment. Triboelectric nanogenerators (TENGs) have proven to be a stable and promising technology for harvesting ambient wind energy. This study explores a new method for the performance enhancement and practical application of TENGs. An array of flag-type triboelectric nanogenerators (F-TENGs) for harvesting wind energy is proposed. An F-TENG consists of one piece of polytetrafluoroethylene (PTFE) membrane, which has two carbon-coated polyethylene terephthalate (PET) membranes on either side with their edges sealed. The PTFE was pre-ground to increase the initial charge on the surface and to enhance the effective contact area by improving the surface roughness, thus achieving a significant improvement in the output performance. The vertical and horizontal arrays of F-TENGs significantly improved the power output performance. The optimal power output performance was achieved when the vertical parallel distance was approximately 4D/15 (see the main text for the meaning of D), and the horizontal parallel distance was approximately 2D. We found that the peak output voltage and current of a single flag-type TENG of constant size were increased by 255% and 344%, respectively, reaching values of 64 V and 8 μA, respectively. Full article

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. Full article

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 NO_X, 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 NO_X Tier II standards were given. The results showed that the main engine fuel-based emission factors of NO_X, 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 NO_X, 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. Full article

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. Full article