Search results

The purpose of this paper is to study the influence of lubricant temperature-viscosity on the performance for a hydrodynamic journal floating ring bearing (FRB), including ring-journal speed ratio and stability.

The finite difference method was used to solve computational models of Reynolds equation, energy equation and temperature–viscosity equation. Dynamic coefficients were obtained based on the floating ring balance. The dynamic model of journal and floating ring was established to deduce the stability criterion of single mass symmetrical rigid FRB rotor system by the Routh–Hurwitz method. The outlet temperature and ring-journal speed ratio under different journal speeds were compared to experimental data.

The temperature–viscosity effect reduces the ring-journal speed ratio and stability of rotor system. According to theoretical and experimental results, the outlet temperature rises and ring-journal ratio drops when the journal speed rises.

The temperature–viscosity effect is combined with dynamic characteristics to analyze the stability of the rotor system and lubrication mechanism for an FRB. Influence of temperature–viscosity on the ring-journal ratio and multi-stable regions of system are studied.

This paper aims to study the influence of thermal effect on the performance for a high-speed conical hybrid bearing including stability and minimum oil film thickness.

A thermal hydrodynamic (THD) model and dynamic model of single mass rigid rotor system were established by taking conical hybrid bearing with shallow and deep pockets as the research object, dynamic coefficient and stability parameters of bearing-rotor system were obtained by using finite element method (FEM) and finite difference method (FDM) to solve computational models of Reynolds equation, energy equation and viscosity-temperature equation. Minimum oil film thickness was obtained based on bearing force balance. Dynamic coefficient was compared with previous findings.

After considering thermal effect, the dimensionless critical mass decreases, a significant decrease in the instability speed, and the stability of the system decreases greatly; the minimum oil film thickness decreases because of thermal effect.

The thermal effect is combined with dynamic characteristics to analyze stability of the rotor system for a conical hybrid bearing. Influence of thermal effect on minimum oil film thickness is studied.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2019-0542/

Abnormal changes in temperature directly affect the stability and reliability of a gyroscope. Predicting the temperature and detecting the abnormal change is great value for timely understanding of the working state of the gyroscope. Considering that the actual collected gyroscope shell temperature data have strong non-linearity and are accompanied by random noise pollution, the prediction accuracy and convergence speed of the traditional method need to be improved. The purpose of this paper is to use a predictive model with strong nonlinear mapping ability to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

In this paper, an double hidden layer long-short term memory (LSTM) is presented to predict temperature data for the gyroscope (including single point and period prediction), and the evaluation index of the prediction effect is also proposed, and the prediction effects of shell temperature data are compared by BP network, support vector machine (SVM) and LSTM network. Using the estimated value detects the abnormal change of the gyroscope.

By combined simulation calculation with the gyroscope measured data, the effect of different network hyperparameters on shell temperature prediction of the gyroscope is analyzed, and the LSTM network can be used to predict the temperature (time series data). By comparing the performance indicators of different prediction methods, the accuracy of the shell temperature estimation by LSTM is better, which can meet the requirements of abnormal change detection. Quick and accurate diagnosis of different types of gyroscope faults (steps and drifts) can be achieved by setting reasonable data window lengths and thresholds.

The LSTM model is a deep neural network model with multiple non-linear mapping levels, and can abstract the input signal layer by layer and extract features to discover deeper underlying laws. The improved method has been used to solve the problem of strong non-linearity and random noise pollution in time series, and the estimated value can detect the abnormal change of the gyroscope.

In this paper, based on the LSTM network, an double hidden layer LSTM is presented to predict temperature data for the gyroscope (including single point and period prediction), and validate the effectiveness and feasibility of the algorithm by using shell temperature measurement data. The prediction effects of shell temperature data are compared by BP network, SVM and LSTM network. The LSTM network has the best prediction effect, and is used to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

This research outlines the Hong Kong film industry with examination of key actors, directors, films, and production companies within the martial arts genre of Hong Kong Action Cinema. Hong Kong Film Award winners and nominees, core films within genres, and core reference works both general and theoretical from experts in the field of Hong Kong martial arts film research have been highlighted. Web sites are suggested that provide reviews of Hong Kong martial arts films, biographical information on a variety of actors and actresses as well as comprehensive bibliographic information on select films. Also included are commercial Web sites that provide Hong Kong martial arts films.

The effect of different service parameters on the current-carrying tribological properties of CF-Al₂O₃/Cu composites was investigated, and the damage behavior of the composites under different service parameters was probed. The purpose of this study is to provide a theoretical basis for the application of CF-Al₂O₃/Cu composites.

The composites were fabricated by internal oxidation combined with powder metallurgy. The current-carrying tribological properties of CF-Al₂O₃/Cu composites were investigated on an electrical damage test system at different loads and currents.

As the load increases, the wear mechanism of the composite changes from abrasive wear to delamination wear. As the current increases, the oxidation wear and arc erosion of the composites gradually intensified. Under the service parameters of 0–25 A and 30–40 N, the composite has relatively stable current-carrying tribological properties.

This paper could provide a theoretical basis for the practical application of CF-Al₂O₃/Cu composites.

The similarity measurement of time series is an important research in time series detection, which is a basic work of time series clustering, anomaly discovery, prediction and many other data mining problems. The purpose of this paper is to design a new similarity measurement algorithm to improve the performance of the original similarity measurement algorithm. The subsequence morphological information is taken into account by the proposed algorithm, and time series is represented by a pattern, so the similarity measurement algorithm is more accurate.

Following some previous researches on similarity measurement, an improved method is presented. This new method combines morphological representation and dynamic time warping (DTW) technique to measure the similarities of time series. After the segmentation of time series data into segments, three parameter values of median, point number and slope are introduced into the improved distance measurement formula. The effectiveness of the morphological weighted DTW algorithm (MW-DTW) is demonstrated by the example of momentum wheel data of an aircraft attitude control system.

The improved method is insensitive to the distortion and expansion of time axis and can be used to detect the morphological changes of time series data. Simulation results confirm that this method proposed in this paper has a high accuracy of similarity measurement.

This improved method has been used to solve the problem of similarity measurement in time series, which is widely emerged in different fields of science and engineering, such as the field of control, measurement, monitoring, process signal processing and economic analysis.

In the similarity measurement of time series, the distance between sequences is often used as the only detection index. The results of similarity measurement should not be affected by the longitudinal or transverse stretching and translation changes of the sequence, so it is necessary to incorporate the morphological changes of the sequence into similarity measurement. The MW-DTW is more suitable for the actual situation. At the same time, the MW-DTW algorithm reduces the computational complexity by transforming the computational object to subsequences.

Fire smoke detection in petrochemical plant can prevent fire and ensure production safety and life safety. The purpose of this paper is to solve the problem of missed detection and false detection in flame smoke detection under complex factory background.

This paper presents a flame smoke detection algorithm based on YOLOv5. The target regression loss function (CIoU) is used to improve the missed detection and false detection in target detection and improve the model detection performance. The improved activation function avoids gradient disappearance to maintain high real-time performance of the algorithm. Data enhancement technology is used to enhance the ability of the network to extract features and improve the accuracy of the model for small target detection.

Based on the actual situation of flame smoke, the loss function and activation function of YOLOv5 model are improved. Based on the improved YOLOv5 model, a flame smoke detection algorithm with generalization performance is established. The improved model is compared with SSD and YOLOv4-tiny. The accuracy of the improved YOLOv5 model can reach 99.5%, which achieves a more accurate detection effect on flame smoke. The improved network model is superior to the existing methods in running time and accuracy.

Aiming at the actual particularity of flame smoke detection, an improved flame smoke detection network model based on YOLOv5 is established. The purpose of optimizing the model is achieved by improving the loss function, and the activation function with stronger nonlinear ability is combined to avoid over-fitting of the network. This method is helpful to improve the problems of missed detection and false detection in flame smoke detection and can be further extended to pedestrian target detection and vehicle running recognition.

Copper matrix composites are widely used in high-voltage switches, electrified railways and other electric friction fields. The purpose of this study is to improve its wear resistance and investigate the effect of hybrid carbon nanotubes (CNTs) and titanium diboride (TiB₂) particles reinforced copper matrix composites on electrical wear performance.

CNTs and TiB₂ particles were introduced into copper matrix simultaneously by powder metallurgy combined with electroless copper plating. Electrical wear performance of the composites was studied on self-made pin on disk electrical wear tester.

The results show that the friction coefficient and wear rate of (1CNTs–4TiB₂)/Cu composite are respectively reduced by 40% and 25.3%, compared with single TiB₂/Cu composites. The micron-sized TiB₂ particles can hinder the plastic deformation of composites, and bear part of the load to weaken the wear rate of composites. CNTs with the self-lubricating property can form lubricating layer to reduce the friction coefficient of composites.

This work can provide a design method for further improving the wear properties of TiB₂/Cu composites.

This study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training.

The research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation.

Sensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training.

The integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

According to the nature and source of alternative energy, types of new energy vehicle include hybrid electric vehicle, pure electric vehicle, fuel cell vehicles, natural gas vehicle, coal‐to‐alcohol vehicle, bio‐fuel vehicle, clean diesel vehicle and so on. At present, all kinds of new energy vehicle are flourishing, but in the near future it is not sure what kind of new energy vehicle that national policies will support, so the risk of new energy vehicle project is large. This paper aims to address this issue.

Grey target decision‐making mainly was used in the choice of programs. First, the decision‐making matrix and the index weight are determined after establishing the decision‐making index system; then the positive clout and the negative clout are determined; the distance to the positive clout and the negative clout for every decision‐making program are calculated; finally, the composite distance to clout is obtained and programs sorted in light of the size of composite distance to target centre.

This paper analyzed the operating conditions of new energy vehicle in Zhengzhou and Xinxiang; and then elaborated the modelling steps of weighted grey target decision with both the positive clout and the negative clout, and used in recent decision‐making the new energy vehicle in Henan Province. It not only makes more comprehensive and rational decision‐making, but also extends the application of the grey target decision model in practice.

In the recent two years of formulating national and local relevant preferential policies, the enthusiasm of enterprises to participate and the level of consumer awareness are further improved; as an international trend, the development of a new energy vehicle is inevitable.

It helps to make scientific decision‐making. The study proposes a theoretical guidance to carry out the new energy vehicle project in Henan Province.

This paper elaborates the modelling steps of weighted grey target decision‐making based on positive clout and negative clout, and established the model of grey target decision‐making according to the selected index; finally, it analyzes the recent development route of a new energy vehicle in Henan Province and puts forward some suggestions.