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.

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.

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.

With the development of autonomous/smart technologies and the Internet of Things (IoT), tremendous wireless sensor nodes (WSNs) are of great importance to realize intelligent mechanical engineering, which is significant in the industrial and social fields. However, current power supply methods, cable and battery for instance, face challenges such as layout difficulties, high cost, short life, and environmental pollution. Meanwhile, vibration is ubiquitous in machinery, vehicles, structures, etc., but has been regarded as an unwanted by‐product and wasted in most cases. Therefore, it is crucial to harvest mechanical vibration energy to achieve in situ power supply for these WSNs. As a recent energy conversion technology, triboelectric nanogenerator (TENG) is particularly good at harvesting such broadband, weak, and irregular mechanical energy, which provides a feasible scheme for the power supply of WSNs. In this review, recent achievements of mechanical vibration energy harvesting (VEH) related to mechanical engineering based on TENG are systematically reviewed from the perspective of contact–separation (C‐S) and freestanding modes. Finally, existing challenges and forthcoming development orientation of the VEH based on TENG are discussed in depth, which will be conducive to the future development of intelligent mechanical engineering in the era of IoT.

Measurement while drilling (MWD) system of offshore drill pipe is of great significance for safety production. It is greatly demanded to develop a sustainable power supply as well as self‐powered sensors for the MWD system. In this work, a drill pipe‐embedded annular type triboelectric nanogenerator (AT‐TENG) is proposed and systematically investigated, and it consists of an annular silicone rubber and a pair of electrodes. The design of the AT‐TENG allows the silicone rubber to contact and separate with the electrodes easily in a narrow space under arbitrary direction vibration to realize power generation. The experimental results show that the peak power density of AT‐TENG with single‐pair electrode reaches 63.7 W m−3 under the vibration frequency of 4 Hz and amplitude of 100 mm. The minimum peak voltage is >65 V within 360° lateral vibration range. The peak voltage achieves 351.62 V under the third‐order natural frequency. In addition, the amplitude and frequency obtained from the voltage signals exhibit linearly with those of actual vibration. The mean error between the frequency detected by AT‐TENG and actual frequency is <0.03%. Thus, the AT‐TENG can serve as an energy harvester and self‐powered vibration sensor.

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.

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.

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