不锈钢马氏体转变的影响因素研究

对S30408和S30403两种牌号的奥氏体不锈钢板材在室温条件下进行单向拉伸试验,并用铁素体测量仪测量其马氏体相变量,结果表明随着拉伸变形量的增加,都诱发产生了形变马氏体,且随着变形量的增加形变诱发马氏体量迅速增加,并在拉断前逐渐趋于饱和。同时表明,随着含碳量增加马氏体转变量反而减小。     

基于DCS的换热站管网监控系统设计与实现

针对华能青岛热电新建小区供暖管网的工艺流程和实际需要，以Opto 22为开发平台，设计开发了基于DCS的换热站管网监控系统。该系统以新建小区换热站为实际监控对象，可以实现对换热站供暖管网各监控测点的数据采集和过程控制，实现对流量、温度和压力的数据显示，人机界面操作和对整个工艺流程的控制管理。系统采用经典的DCS设计方法，实现集中监控、分散控制。相比于其他PLC监控平台，系统具有目标用户针对性强，在保证系统安全可靠运行的情况下具有操作简单易学易用的特点,设计内容完全符合华能青岛热电新建小区的要求。     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

We present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.     

Parallel quality-related dynamic principal component regression method for chemical process monitoring

Traditional quality-related process monitoring mainly focuses on the magnitude change of the quality variables caused by additive faults. However, the abnormal fluctuations in the quality variables caused by multiplicative faults are often overlooked. In this paper, a novel parallel dynamic principal component regression (P-DPCR) algorithm is proposed to monitor the changes in the magnitude and fluctuation of the quality variables simultaneously. Firstly, in order to eliminate the interference of quality-unrelated variables, the quality-related process variables are selected on the basis of correlation analysis. Secondly, the dynamic extension and moving window are carried out for process variables and quality variables, in which the dynamic variables space (called X-space/Y-space) and the variance space (called VX-space/VY-space) are constructed. Afterwards, double quality-related statistics based on the regression model of these four spaces are given, and the comprehensive monitoring decision can be obtained. Finally, two numerical cases and the Tennessee Eastman process are used to show the effectiveness of the proposed method.     

Effect of plastic deformation at room temperature on hydrogen diffusion of S30408

Understanding the influence of plastic deformation on diffusion is critical for hydrogen embrittlement (HE) study. In this work, thermal desorption spectroscope (TDS), slow strain rate test (SSRT), feritscope, transmission electron microscope (TEM) and TDS model were used to study the relation between plastic deformation and hydrogen diffusion, aiming at unambiguously elucidating the effect of plastic deformation on hydrogen diffusion of austenitic stainless steel, S30408. An effective method was developed to deduce apparent hydrogen diffusion coefficient of austenitic stainless steel in this paper. Results indicate apparent hydrogen diffusion coefficient decreases firstly and then increases with increasing plastic deformation at room temperature. Hydrogen diffusion effected by plastic deformation is a complicated process which is suggested to be divided into two processes controlled by dislocation and strain-induced martensite, respectively, and the transition point is about 20% strain demonstrated by experiments in this case.     

24小时尿钠排泄与难治性高血压患者血压达标状态的相关性:单中心横断面研究

目的分析24 h尿钠排泄与难治性高血压(resistant hypertension,RH)患者诊室血压及家庭自测血压(home blood pressure monitoring,HBPM)达标状态的关系。方法通过北京协和医院心内科门诊RH数据库,收集2017年10月至2018年3月间在北京协和医院心内科门诊就诊的RH患者临床资料,进行横断面调查。检测患者24 h尿钠,记录患者同期诊室血压、HBPM水平及临床用药情况,以所有患者24 h尿钠水平四分位数为分界点,将研究对象分为低尿钠、低-中尿钠、中-高尿钠及高尿钠4组。采用多因素Logistic回归,分析影响RH血压达标状态的危险因素。结果共202例RH患者入选本研究,男性107例,女性95例,平均年龄(59. 87±16. 30)岁。24 h尿钠平均水平为(198. 92±96. 59) mmol,年轻患者及体质量指数高者尿钠水平更高(P均<0. 001)。随尿钠升高,降压药物的服用种类显著增多(P=0. 001),早晨及上午的HBPM达标率低(P=0. 040,0. 032)。多因素Logistic回归分析显示,24 h尿钠水平与诊室血压(OR=2. 356,95%CI:1. 004～5. 533,P=0. 049),HBPM早晨血压(OR=2. 408,95%CI:1. 026～5. 650,P=0. 030)及HBPM上午血压(OR=2. 299,95%CI:1. 031～5. 129,P=0. 033)达标状态独立相关,而与下午及夜间HBPM血压达标状态无显著相关(P均>0. 05)。结论 24 h尿钠是RH患者诊室血压及HBPM早晨及上午血压达标的独立相关因素,限制钠盐摄入对减少RH患者血压波动、促进血压达标有重要作用。     

基于载波相位差分的形变监测高精度定位算法

传统载波相位差分算法在形变监测领域适用性不足，实时动态定位（RTK）精度难以满足要求，而载波双差静态相对定位连续解算时形变跟踪性能较低等。针对这些问题，在对动静态算法深入研究的基础上，提出一种基于载波相位差分的动静态自适应融合算法。通过方差变化法实时判断定位结果是否收敛，自适应调节扩展卡尔曼滤波（EKF）状态先验估计过程。在收敛时刻增大位置参数的先验估计误差的协方差值，使EKF后验估计过程倾向于信赖测量值；未收敛时刻通过EKF迭代，使EKF后验估计过程倾向于信赖状态预测值。实验结果表明：相比传统RTK新算法精度有明显提高，水平达±2 mm内，高程达到±4 mm内；相比静态定位则缩减了观测周期，提高了微小形变跟踪性能。     

气控环形防喷器密封胶芯大变形分析

针对气控环形防喷器胶芯在密封过程中的大变形以及与油管存在密封压力难以计算的问题,建立胶芯物理模型,结合厚壁筒理论与橡胶本构方程,同时考虑胶芯的材料非线性和几何非线性,探讨防喷器密封过程中胶芯变形与接触压力的变化。根据胶芯变形过程中无变形阶段、自由变形阶段和接触变形阶段的不同受力状况,利用迭代的方法对每个离散微段施加控制压力,得到不同控制压力下胶芯变形量与接触压力,并与仿真结果进行比较。结果表明:在自由变形阶段,随控制压力增加,胶芯变形量逐渐增大且由中间向两侧逐渐减小;在接触变形阶段,胶芯内径保持不变,且与油管间接触压力不断增加。采用胶芯变形理论方法计算的接触压力变化规律与仿真结果基本相同,验证了胶芯变形理论计算方法的正确性。     

海绵城市建设PPP项目委托代理契约分析

针对海绵城市PPP(Public-Private Partnership)项目建设、运营管理问题,为了降低由于项目参与的各方掌握信息、地位的不对称而产生的道德风险、维护各参与方的合法权益,基于一定的前提假设,构建海绵城市建设PPP项目契约模型,利用优化理论分别从代理企业和政府方两个主体的效用对模型的各个参数相关关系进行了分析,并利用数值模拟讨论了监控机制各参数对政府方监控意愿的影响。最后,结合常德市海绵城市PPP项目建设的案例,分别分析在当前考核机制下项目在建设、运营期可能存在的问题,并提出了相应的对策以解决这些问题。结果表明:代理企业的监控和激励措施具有互补性,海绵工程项目风险来源广泛,企业为规避风险会偏好零风险的固定收益,设定监控机制能激励企业更加努力的工作。