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Vehicle fault diagnosis is a key factor in ensuring the safe and efficient operation of the railway system. Due to the numerous vehicle categories and different fault mechanisms, there is an unbalanced fault category problem. Most of the current methods to solve this problem have complex algorithm structures, low efficiency and require prior knowledge. This study aims to propose a new method which has a simple structure and does not require any prior knowledge to achieve a fast diagnosis of unbalanced vehicle faults.

This study proposes a novel K-means with feature learning based on the feature learning K-means-improved cluster-centers selection (FKM-ICS) method, which includes the ICS and the FKM. Specifically, this study defines cluster centers approximation to select the initialized cluster centers in the ICS. This study uses improved term frequency-inverse document frequency to measure and adjust the feature word weights in each cluster, retaining the top τ feature words with the highest weight in each cluster and perform the clustering process again in the FKM. With the FKM-ICS method, clustering performance for unbalanced vehicle fault diagnosis can be significantly enhanced.

This study finds that the FKM-ICS can achieve a fast diagnosis of vehicle faults on the vehicle fault text (VFT) data set from a railway station in the 2017 (VFT) data set. The experimental results on VFT indicate the proposed method in this paper, outperforms several state-of-the-art methods.

This is the first effort to address the vehicle fault diagnostic problem and the proposed method performs effectively and efficiently. The ICS enables the FKM-ICS method to exclude the effect of outliers, solves the disadvantages of the fault text data contained a certain amount of noisy data, which effectively enhanced the method stability. The FKM enhances the distribution of feature words that discriminate between different fault categories and reduces the number of feature words to make the FKM-ICS method faster and better cluster for unbalanced vehicle fault diagnostic.

In order to improve the robustness to noise in point cloud plane fitting, a combined model of improved Cook’s distance (ICOOK) and WTLS is proposed by setting a modified Cook’s increment, which could help adaptively remove the noise points that exceeds the threshold.

This paper proposes a robust point cloud plane fitting method based on ICOOK and WTLS to improve the robustness to noise in point cloud fitting. The ICOOK to denoise the initial point cloud was set and verified with experiments. In the meanwhile, weighted total least squares method (WTLS) was adopted to perform plane fitting on the denoised point cloud set to obtain the plane equation.

(a) A threshold-adaptive Cook’s distance method is designed, which can automatically match a suitable threshold. (b) The ICOOK is fused with the WTLS method, and the simulation experiments and the actual fitting of the surface of the DD motor are carried out to verify the actual application. (c) The results shows that the plane fitting accuracy and unit weight variance of the algorithm in this paper are substantially enhanced.

The existing point cloud plane fitting methods are not robust to noise, so a robust point cloud plane fitting method based on a combined model of ICOOK and WTLS is proposed. The existing point cloud plane fitting methods are not robust to noise, so a robust point cloud plane fitting method based on a combined model of ICOOK and WTLS is proposed.

Molecular dynamics is an emerging simulation technique in the field of machining in recent years. Many researchers have tried to simulate different processing methods of various materials with the theory of molecular dynamics (MD), and some preliminary conclusions have been obtained. However, the application of MD simulation is more limited compared with traditional finite element model (FEM) simulation technique due to the complex modeling approach and long computation time. Therefore, more studies on the MD simulations are required to provide a reliable theoretical basis for the nanoscale interpretation of grinding process. This study investigates the crystal structures, dislocations, force, temperature and subsurface damage (SSD) in the grinding of iron-nickel alloy using MD analysis.

In this study the simulation model is established on the basis of the workpiece and single cubic boron nitride (CBN) grit with embedded atom method and Morse potentials describing the forces and energies between different atoms. The effects of grinding parameters on the material microstructure are studied based on the simulation results.

When CBN grit goes through one of the grains, the arrangement of atoms within the grain will be disordered, but other grains will not be easily deformed due to the protection of the grain boundaries. Higher grinding speed and larger cutting depth can cause greater impact of grit on the atoms, and more body-centered cubic (BCC) structures will be destroyed. The dislocations will appear in grain boundaries due to the rearrangement of atoms in grinding. The increase of grinding speed results in the more transformation from BCC to amorphous structures.

This study is aimed to study the grinding of Fe-Ni alloy (maraging steel) with single grit through MD simulation method, and to reveal the microstructure evolution within the affected range of SSD layer in the workpiece. The simulation model of polycrystalline structure of Fe-Ni maraging steel and grinding process of single CBN grit is constructed based on the Voronoi algorithm. The atomic accumulation, transformation of crystal structures, evolution of dislocations as well as the generation of SSD are discussed according to the simulation results.

Bearings in electric machines often work in high speed, light load and vibration load conditions. The purpose of this paper is to find a new fatigue damage accumulation rating life model of ball bearings, which is expected for calculating fatigue life of ball bearings more accurately under vibration load, especially in high speed and light load conditions.

A new fatigue damage accumulation rating life model of ball bearings considering time-varying vibration load is proposed. Vibration equations of rotor-bearing system are constructed and solved by Runge–Kutta method. The modified rating life and modified reference rating life model under vibration load is also proposed. Contrast of the three fatigue life models and the influence of dynamic balance level, rotating speed, preload of ball bearings on bearing’s fatigue life are analyzed.

To calculate fatigue rating life of ball bearings more accurately under vibration load, especially in high speed and light load conditions, the fatigue damage accumulation rating life model should be considered. The optimum preload has an obvious influence on fatigue rating life.

This paper used analytical method and model that is helpful for design of steel ball bearing in high speed, light load and vibration load conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2019-0180/

This paper aims to dismantle the idea that sex per se explains entrepreneurial outcomes and demonstrates the influence of a gendered motivation on forging and shaping new venture teams, which is a disruptive choice affecting the future of start-ups.

A two-level research model is validated on data from the Panel Study of Entrepreneurial Dynamics II (PSED II), with a system of simultaneous equations. First, if team features affect the performance of new ventures is tested; then, the study investigates determinants of team features with a focus on sex and motivation of nascent entrepreneurs.

Human capital (HC) in terms of education and experience of team members consistently explains venture evolution only when considering the larger team of affiliates. The HC gathered by nascent entrepreneurs is not because of the simplistic sex condition, but rather to a gendered motivation related to the inferior need of achievement of women.

Limitations of discretionary scoring assigned to items of the PSED II survey are present, but unavoidable when processing qualitative data.

Women need to be (culturally) educated on how to re-balance their personal motivation towards entrepreneurship by fostering their incentives for achievement. Political and educational programmes could trigger success in the creation of new businesses led by women.

This paper contributes to the literature on nascent entrepreneurship, focusing on the entrepreneurial teams in the initial phase of business creation, and provides the basis for further studies aimed at eradicating the stereotypes of gender roles that lead women to self-exclusion and organizational errors.

The purpose of this paper is to analyze the supply chain's resilience in crowd networks from both static and dynamic perspectives.

This paper first defines the supply chain’s resilience, then proposes a graphical and game-theoretic framework to evaluate the resilience.

In this framework, an equilibrium with high resilience will be achieved after the iterated prisoner's dilemma in the supply chain. The two-stage update mechanism contributes to higher profits, higher stability and stronger risk resistance capability. The reputation-based tit-for-tat strategy in the second stage helps to realize society cooperation.

This work pays more attention to the dynamic evolution of interactions between organizations in the supply chain. It provides an important theoretical basis for future work such as how to effectively control and guide the evolution of events in the intelligence network and how to stand sudden changes and avoid collapse.

This study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics.

The authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper.

The research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality.

Previous literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.

This paper aims to build a scientific evaluation index system for regional low-carbon circular economic development. Taking Sichuan Province as the empirical research object, the paper evaluates its low-carbon circular economy (LCCE) development level and proposes policy recommendations for climate change improvement based on the evaluation results.

This paper, first, built an evaluation index system with 30 indicators within six subsystems, namely, economic development, social progress, energy consumption, low-carbon emissions, carbon sink capacity and environmental carrying capacity. Second, develop an “entropy weight-grey correlation” evaluation method. Finally, from a practical point of view, measure the development level of LCCE in Sichuan Province, China, from 2008 to 2018.

It was found that Sichuan LCCE development had a general downward trend from 2008 to 2012 and a steady upward trend from 2012 to 2018; however, the overall level was low. The main factors affecting the LCCE development are lagging energy consumption and environmental carrying capacity subsystem developments.

This paper puts forward relevant suggestions for improving the development of a low-carbon economy and climate change for the reference of policymakers.

This paper built an evaluation index system with 30 indicators for regional low carbon circular economic development. The evaluation method of “entropy weight-grey correlation” is used to measure the development level of regional LCCE in Sichuan Province, China.