Mechanisms of martensitic transformations in boron nitride and conditions of their development

Abstract

The dependence of the pressure threshold of martensitic transformations on the disordering degree of starting structures is considered for graphite-like BN into diamond-like BN modifications transitions. The effect of loading conditions on transformation mechanisms of rhombohedral BN into zinc blende or wurtzite modifications is analyzed also. Analytical relations obtained allow to explain the experimental data and to predict a behavior of various graphite-like structures under different p, T conditions.     

Experimental and numerical investigations on the pure material instability of an Oldroyd's 3-constant model

Received March 25, 2001 / Published online November 15, 2001     

Shear viscosity of polymer and surfactant monolayers

The surface shear viscosity of monolayers formed at the surface of water by adsorbed polyethyl- eneoxyde and by stearic acid is measured as a function of the surface pressure of the monolayer using a new surface viscometer. The principle of the viscometer is the measurement of the drag force on a circular disk undergoing a uniform translation at the water surface: a hydrodynamic model based on the lubrication approximation allows a calculation of the surface viscosities from the absolute measurement of the drag forces. Received: 26 August 1999     

新疆富蕴县萨尔布拉克金矿预测区断裂特征及其控矿规律

在全面、系统收集和研究新疆富蕴县萨尔布拉克地区地质、地球物理、地球化学、矿产普查和科研资料的基础上，深入分析金矿成矿地质条件、控矿因素和找矿方法组合，对萨尔布拉克预测区95条断裂的地质、地球物理、地球化学特征进行了全面统计分析，确定了42条赋金断裂．其中早期断裂8条，占19％；晚期断裂34条，占81％．赋金断裂以晚期南北向为主，次为东西向和北西向．航磁异常向上延拓高度大者，即赋金断裂延深大者对金的成矿有利．     

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.     

A New Deep Dual Temporal Domain Adaptation Method for Online Detection of Bearings Early Fault

With the quick development of sensor technology in recent years, online detection of early fault without system halt has received much attention in the field of bearing prognostics and health management. While lacking representative samples of the online data, one can try to adapt the previously-learned detection rule to the online detection task instead of training a new rule merely using online data. As one may come across a change of the data distribution between offline and online working conditions, it is challenging to utilize the data from different working conditions to improve detection accuracy and robustness. To solve this problem, a new online detection method of bearing early fault is proposed in this paper based on deep transfer learning. The proposed method contains an offline stage and an online stage. In the offline stage, a new state assessment method is proposed to determine the period of the normal state and the degradation state for whole-life degradation sequences. Moreover, a new deep dual temporal domain adaptation (DTDA) model is proposed. By adopting a dual adaptation strategy on the time convolutional network and domain adversarial neural network, the DTDA model can effectively extract domain-invariant temporal feature representation. In the online stage, each sequentially-arrived data batch is directly fed into the trained DTDA model to recognize whether an early fault occurs. Furthermore, a health indicator of target bearing is also built based on the DTDA features to intuitively evaluate the detection results. Experiments are conducted on the IEEE Prognostics and Health Management (PHM) Challenge 2012 bearing dataset. The results show that, compared with nine state-of-the-art fault detection and diagnosis methods, the proposed method can get an earlier detection location and lower false alarm rate.     

Adaptive Diagnosis for Fault Tolerant Data Fusion Based on α-Rényi Divergence Strategy for Vehicle Localization

When applying a diagnostic technique to complex systems, whose dynamics, constraints, and environment evolve over time, being able to re-evaluate the residuals that are capable of detecting defaults and proposing the most appropriate ones can quickly prove to make sense. For this purpose, the concept of adaptive diagnosis is introduced. In this work, the contributions of information theory are investigated in order to propose a Fault-Tolerant multi-sensor data fusion framework. This work is part of studies proposing an architecture combining a stochastic filter for state estimation with a diagnostic layer with the aim of proposing a safe and accurate state estimation from potentially inconsistent or erroneous sensors measurements. From the design of the residuals, using α-Rényi Divergence (α-RD), to the optimization of the decision threshold, through the establishment of a function that is dedicated to the choice of α at each moment, we detail each step of the proposed automated decision-support framework. We also dwell on: (1) the consequences of the degree of freedom provided by this α parameter and on (2) the application-dictated policy to design the α tuning function playing on the overall performance of the system (detection rate, false alarms, and missed detection rates). Finally, we present a real application case on which this framework has been tested. The problem of multi-sensor localization, integrating sensors whose operating range is variable according to the environment crossed, is a case study to illustrate the contributions of such an approach and show the performance.     

Effect of surface morphology on measurement and interpretation of boundary slip on superhydrophobic surfaces

Highly liquid repellent surfaces have been obtained by the combination of roughness and hydrophobicity. Studies have reported that the flow over such surfaces exhibits larger boundary slip as compared to the smooth hydrophobic surfaces. However, the surface roughness can also lead to apparent slip. Thus, the effect of the two factors, that is, wettability and roughness, needs to be segregated. In this study, we have measured the slippage of water on rough hydrophilic and hydrophobic surfaces using colloidal probe atomic force microscopy technique (CP‐AFM). Results showed that the effect of surface roughness on the measured slip is dominant over that of wettability. It was also found that slip on surfaces with sparsely distributed asperities is highly local and measurements on various locations give dissimilar results. The results suggested that the main reason of the larger slip, on rough hydrophobic surfaces, is likely to be the roughness and not the hydrophobicity. Moreover, it was also found that the slip does not vary considerably with the increase or decrease in the shear rate. Most likely, this kind of slip phenomena is caused by the apparent decrease of the drag force, because the nanoasperities on the surface restrict the probe from reaching the surface properly. Copyright © 2016 John Wiley & Sons, Ltd.     

封隔器卡瓦接触应力研究

封隔器上的卡瓦锚定后起到支撑封隔器、锁定胶皮筒的作用,其性能好坏直接影响到油井的产量和生产安全。卡瓦与套管间的咬合力分布决定了封隔器在使用中的成败。咬合力的分布是否合理,将直接关系到套管的损伤程度及卡瓦的寿命。文中同时应用有限元法和三维光弹性技术对封隔器卡瓦进行了接触应力分析,介绍了实验模型的建立和实验步骤以及计算模型的建立和边界条件的考虑,比较了数值计算与三维光弹实验的结果,分析了造成误差的原因。实验应力分析结果为有限元计算模型的建立及边界条件的确定提供了重要的实验保证。研究工作获得了一些重要的结果,为封隔器卡瓦的优化设计提供了相关的参数。     

基于振动监测的静载荷滑动轴承接触摩擦故障诊断实验研究

采用静载荷滑动轴承试验台模拟轴颈-轴承从液体润滑状态逐步向干摩擦状态过渡时的接触摩擦故障的发生和发展过程,进而利用振动信号诊断滑动轴承的接触摩擦故障;通过对特征参数进行归一化处理得到无量纲特征参数,进而通过无量纲特征参数的适当数学组合得到无量纲诊断准则,并探讨了无量纲诊断准则的适用性.结果表明:利用所得到的无量纲诊断准则进行轴颈-轴承接触摩擦故障诊断时无须建立用于模式识别的标准模,可实现快捷方便的故障诊断;无量纲诊断准则对工况不敏感,而对故障更敏感,适用于可变工况下滑动轴承的故障诊断,且诊断成本较低.