As one of the important friction pairs of a diesel engine, the cylinder-liner piston ring (CL-PR) faces a harsh high-temperature working environment. To explore the mapping relationship between the friction performance of the CL-PR and the change in temperature, the reciprocating-friction and wear-testing machine was used to analyze the friction performance and lubrication performance of four kinds of lubricating oil at different temperatures (room temperature, 60 °C, 90 °C, and 120 °C) from the friction coefficient, contact resistance and surface topography. The results show that the tribological properties of the four lubricating oils show different trends with the increase in temperature. The friction coefficient of the base oil first decreases and then increases with the increase in temperature; this shows that the friction property of the base oil is improved by a certain temperature rise, and the increase in temperature promotes the formation of an oxide film and reduces the friction coefficient. While the friction coefficient of other three lubricating oils with specific application scenarios increases first and then remains stable, the wear of the friction pair is the most severe at 120 °C. The wear forms are abrasive wear and adhesive wear.

Condition based maintenance(CBM) issues a new challenge of real-time monitoring for machine health maintenance. Wear state monitoring becomes the bottle-neck of CBM due to the lack of on-line information acquiring means. The wear mechanism judgment with characteristic wear debris has been widely adopted in off-line wear analysis; however, on-line wear mechanism characterization remains a big problem. In this paper, the wear mechanism identification via on-line ferrograph images is studied. To obtain isolated wear debris in an on-line ferrograph image, the deposition mechanism of wear debris in on-line ferrograph sensor is studied. The study result shows wear debris chain is the main morphology due to local magnetic field around the deposited wear debris. Accordingly, an improved sampling route for on-line wear debris deposition is designed with focus on the self-adjustment deposition time. As a result, isolated wear debris can be obtained in an on-line image, which facilitates the feature extraction of characteristic wear debris. By referring to the knowledge of analytical ferrograph, four dimensionless morphological features, including equivalent dimension, length-width ratio, shape factor, and contour fractal dimension of characteristic wear debris are extracted for distinguishing four typical wear mechanisms including normal, cutting, fatigue, and severe sliding wear. Furthermore, a feed-forward neural network is adopted to construct an automatic wear mechanism identification model. By training with the samples from analytical ferrograph, the model might identify some typical characteristic wear debris in an on-line ferrograph image. This paper performs a meaningful exploratory for on-line wear mechanism analysis, and the obtained results will provide a feasible way for on-line wear state monitoring.

Aiming at the problem that the traditional water-lubricated bearing cannot carry the heavy load and adapt to the constantly changing operating conditions for the high-power Rim Driven Thruster (RDT), the principle structure of the Magnetic Water-double-suspension Elastic-support Thrust Bearing (MWETB) is designed and the optimal structure parameters of the bearing are selected using simulation. To demonstrate the reliability of the MWETB under the RDTs’ actual working conditions, performance tests, which include the magnetic flux density, magnetic force, and lubrication performance, are carried out. The simulation and experimental results indicate that the optimal offset ratios are in two intervals, and the magnetic alignment and sheath materials have a great effect on the load reduction. The load-carrying force has obvious zoning characteristics with the change in bearing clearance. Besides, compared with the water-lubricated thrust bearings, the MWETB has advantages in terms of minimum film thickness and friction coefficient.

This study proposes a new nonlinear dynamic model of rolling bearing faults based on a collision impact system. The dynamic model accounts for the collision impact system consisting of the rolling elements and localized faults according to the nonlinear Hertzian contact. First, considering the impact of the rolling element and fault structure, the collision impact system between rolling element and localized fault is established, and the vibration responses of the collision impact system can be obtained. Second, the overall rolling bearing is treated as a mass-spring model, and the contact between the rolling element and raceway is treated as a nonlinear spring that conforms to the Hertzian contact deformation theory. Third, according to the Lagrange equation, overall potential energy, overall kinetic energy, elastic potential energy, and kinetic energy of the collision impact system are used to describe the vibration characteristics. Considering the impact of collision impact systems, a nonlinear dynamic model of rolling bearing faults is established. The simulated acceleration results based on the nonlinear dynamic model are compared to experimental results. The comparison indicates that the numerical model can be used to predict the vibration characteristics of rolling bearings faults effectively.

Ship engine misfire faults not only pose a serious threat to the safe operation of ships but may also cause major safety accidents or even lead to ship paralysis, which brings huge economic losses. Most traditional fault diagnosis methods rely on manual experience, with limited feature extraction capability, low diagnostic accuracy, and poor adaptability, which make it difficult to meet the demand for high-precision diagnosis. To this end, a fusion intelligent diagnostic model—ResNet–BiLSTM—is proposed based on a residual neural network (ResNet) and a bidirectional long short-term memory network (BiLSTM). Firstly, a multi-scale decomposition of the instantaneous rotational speed signal of a ship’s engine is carried out by using the continuous wavelet transform (CWT), and features containing misfire fault information are extracted. Subsequently, the extracted features are fed into the ResNet–BiLSTM model for learning. Finally, the intelligent diagnosis of ship dual-fuel engine misfire faults is realized by the classifier. The model combines the advantages of ResNet18 in image feature extraction and the capability of BiLSTM in temporal information processing, which can efficiently capture the time-frequency features and dynamic changes in the fault signal. Through comparison experiments with fusion models AlexNet–BiLSTM, VGG–BiLSTM, and the existing AlexNet–LSTM and VGG–LSTM models, the results show that the ResNet–BiLSTM model outperforms the other models in terms of diagnostic accuracy, robustness, and generalization ability. This model provides an effective new method for intelligent diagnosis of ship dual-fuel engine misfire faults to solve the traditional diagnostic methods’ limitations.

This review provides a comprehensive evaluation of the key technologies and latest advances in cemented carbide bearings for marine environments, such as navigation equipment and deep-sea operations. Given the rigorous performance requirements imposed on bearings by the extreme conditions of marine environments, including high hydrostatic pressure, seawater corrosion and abrasive wear, this paper explores the developments within carbide material systems. It focuses on analyzing the limitations of traditional WC-Co alloys in seawater, as well as the potential and challenges of alternative binder systems such as WC-Ni and WC-high Entropy Alloys (HEAs) in enhancing corrosion resistance and comprehensive mechanical properties. Building on this foundation, the research sorts out the tribological behavior of cemented carbides under seawater lubrication, explaining the influence of the tribocorrosion mechanism on friction characteristics. Meanwhile, it also explores reliability enhancement strategies through surface modifications like coatings and texturing, and discusses the challenges associated with life prediction models. Through tribopair experiments between cemented carbides and various bearing materials, the application orientation of cemented carbides is clarified, which provides a selection framework for carbide bearing applications in different marine scenarios. Finally, the paper summarizes the current technological bottlenecks and core scientific issues, offering insights for future research and development directions in this field.