The vibration signals of rolling bearings are often nonlinear and non-stationary. Multiscale entropy (MSE) has been widely applied to measure the complexity of nonlinear mechanical vibration signals, however, at present only the single channel vibration signals are used for fault diagnosis by many scholars. In this paper multiscale entropy in multivariate framework, i.e., multivariate multiscale entropy (MMSE) is introduced to machinery fault diagnosis to improve the efficiency of fault identification as much as possible through using multi-channel vibration information. MMSE evaluates the multivariate complexity of synchronous multi-channel data and is an effective method for measuring complexity and mutual nonlinear dynamic relationship, but its statistical stability is poor. Refined composite multivariate multiscale fuzzy entropy (RCMMFE) was developed to overcome the problems existing in MMSE and was compared with MSE, multiscale fuzzy entropy, MMSE and multivariate multiscale fuzzy entropy by analyzing simulation data. Finally, a new fault diagnosis method for rolling bearing was proposed based on RCMMFE for fault feature extraction, Laplacian score and particle swarm optimization support vector machine (PSO-SVM) for automatic fault mode identification. The proposed method was compared with the existing methods by analyzing experimental data analysis and the results indicate its effectiveness and superiority.

To investigate the combustion process and reduce Nitric Oxide (NO) emissions in the vertical heating flue of air-staged coke ovens, a three-dimensional computational fluid dynamics method was applied to simulate the combustion process. The model integrates the k-ε turbulence model with a multi-component transport combustion model. The impact of air staging on the flow field and NO emissions in the vertical fire chamber was assessed through comparative validation with experimental data. The impact of air staging on the flow field and NO emissions in the vertical fire chamber was assessed through comparative validation with experimental data. Based on this research, the effects of the excess air coefficient and air inlet distribution ratio on NO emission levels at the flue gas outlet were further investigated. Analysis of the flow field structure, temperature at the center cross-section, component concentration, and NO emission levels indicates that as the excess air coefficient increases, the NO emission levels at the flue gas outlet initially decrease and then increase, accompanied by corresponding changes in outlet temperature. At an air excess factor of 1.3 and an air inlet distribution ratio of 7:3, NO emission levels are at their lowest—53% lower than those in a conventional coke oven—and the temperature distribution in the riser channel is more uniform. These results provide a theoretical foundation for designing the air-staged coke oven standing fire channel structure.

Wear debris is the direct byproduct of frictional interactions between mechanical components. The morphology, quantity, and material characteristics of the debris provide critical information on the wear condition of oil-lubricated systems. Timely detection and monitoring of the debris are critical to preventing further abrasive damage and ensuring reliable operation of the components. Various sensing technologies have been developed to address this challenge. Despite a lot of real-time monitoring research have been done over the past decade, a comprehensive review of online and inline monitoring methods remains lacking. To address this gap, this study presents a systematic bibliometric analysis of recent research on oil wear debris real-time monitoring. The results show that there is a growing interest and rapid development in this field. Key advances in these technologies are reviewed, including electrical sensors, magnetic sensors, acoustic sensors, energy-based sensors, and optical sensors. In addition to analyzing the current literature, this study also highlights the technical limitations and outlines the future research directions. The objective is to provide researchers with an in-depth understanding of the debris monitoring methods in this domain and offer guidance for future research endeavors.

Despite extensive research on head-on droplet collisions over the past decades, detailed investigations into the bouncing behavior of high-viscosity droplets, such as molten glass droplets, are still scarce. In this study, a volume-of-fluid method coupled with dual marker functions is employed to simulate the collision dynamics of molten glass droplets. The results show good agreement with experimental observations in both spatial and temporal dimensions. Theoretical analysis reveals a critical Weber number of 22 for bouncing and coalescence of molten glass droplets with a diameter of 100 μm. Below this threshold, we examine the bouncing behavior across various Weber numbers, categorizing the process into four distinct stages: mutual proximity, radial expansion, suction separation, and reverse separation, and providing a detailed analysis of velocity, pressure, and energy at each stage. As the Weber number increases, vortices sequentially emerge at 4, 8, 12, and 16, suggesting a strong correlation between droplet deformation and vortex generation. At lower Weber numbers, the air film pressure between droplets transitions smoothly between radial expansion and suction separation. However, between Weber numbers 9 and 22, a distinct concave pressure phenomenon is observed during suction separation. Pressure chattering occurs at the beginning of radial expansion and the end of suction separation. Furthermore, the results indicate that the cumulative viscous dissipation energy consistently approaches half of the initial kinetic energy, irrespective of the Weber and Ohnesorge numbers.