On monitoring the process mean of autocorrelated observations with measurement errors using the w-of-w runs-rules scheme

Runs-rules have been widely used since the 1950s in industrial and nonindustrial process monitoring applications to improve the performance of basic and other traditional monitoring schemes. However, none of the studies on runs-rules have accounted for a process with a combined effect of measurement errors and autocorrelation. Hence, in this paper, the use of the w-of-w runs-rules to improve the performance of the Shewhart $\overline{X}$ scheme using an additive model with a constant variance and a first-order autoregressive model is proposed. To reduce the combined negative effect of measurement errors and autocorrelation, we implement a sampling strategy based on rational subgroups in which (a) multiple measurements per item are taken (instead of a standard single measurement) and (b) non-neighboring observations are gathered. Moreover, the latter sampling strategy is incorporated into the values of probability elements of a Markov chain matrix which is used to derive some closed-form expressions for the zero- and steady-state run-length distribution. The main finding of this study is that, with respect to some overall performance measures, the proposed scheme outperforms the existing Shewhart $\overline{X}$ scheme by a significant margin. A real-life example is used to illustrate the practical implementation of the proposed scheme.     

基于改进交叉熵算法的电力系统随机生产模拟

蒙特卡洛模拟的计算效率与系统的可靠性密切相关，在其用于高可靠性系统的随机模拟时存在计算效率偏低的问题。为此，提出了一种基于多层交叉熵与对偶变数抽样技术相结合的随机模拟算法。首先使用多层交叉熵构造零方差概率密度函数的近似函数，提高小概率失效事件的抽取概率；其次基于已构造的近似概率函数，采用对偶变数抽样法进行抽样，进一步提高抽样的收敛速度。以IEEE RTS修改系统为例进行了算例分析，算例结果验证了所提出的基于改进交叉熵的电力系统随机生产模拟算法的有效性。     

Hydrogen quality sampling at the hydrogen refuelling station – lessons learnt on sampling at the production and at the nozzle

Fuel cell electric vehicles and hydrogen refuelling infrastructure are developing quickly in Europe, the USA and Asia. Hydrogen quality for transport applications requires compliance with ISO 14687-2: 2012 and EN 17124:2018 - this needs representative sampling, at the hydrogen production process and at hydrogen dispenser nozzle (which typically fill vehicles to a Nominal Working Pressure of either 35 or 70 MPa). The low thresholds in ISO 14687-2 for oxygen and water can be exceeded if the sampling procedure fails to purge the system sufficiently, which would lead to false results (60% in this study). Purging requirements to remove water were studied using a low pressure sampling rig. For hydrogen dispenser sampling using the Linde H2 Qualitizer (suitable for dispensing pressures up to 70 MPa), purging number and the effect of the initial fill level of a vehicle compressed hydrogen storage system were investigated experimentally to avoid hydrogen quality violation due to oxygen false positive. The study procedure reduces from 60% to 0% hydrogen quality violation. The next challenges highlighted are safe purging and reliable sampling of reactive contaminants in gas cylinders.     

矿浆多参数在线检测系统设计

针对现有矿浆浓度检测系统存在的测量数据单一、取样过程易沉降、难以检验所取样品均匀性等缺点,设计了一种矿浆多参数在线检测系统。基于称重法测量原理实现对矿浆浓度、体积流量、质量流量等多参数的在线检测。使用基于旋流消能法的取样器实现对原矿浆均匀化及防沉降处理。基于水力学孔流原理实现对取样矿浆的均值化处理及均匀性检验。在云南某选矿厂的长期应用结果表明:检测系统所测数据与浓度壶所测数据差值在2%以内,满足工业精度要求。由取样器取出样品的不均匀度在1%以内,取样过程未出现沉降、堵塞等现象,取样效果良好。     

Fault estimation and accommodation for networked control systems with nonuniform sampling periods

This paper deals with the problem of fault estimation and accommodation for a class of networked control systems with nonuniform uncertain sampling periods. Firstly, the reason why the adaptive fault diagnosis observer cannot be applied to networked control systems is analyzed. Based on this analysis, a novel robust fault estimation observer is constructed to estimate both continuous‐time fault and system states by using nonuniformly discrete‐time sampled outputs. Furthermore, using the obtained states and fault information, a nonuniformly sampled‐data fault tolerant control law is designed to preserve the stability of the closed‐loop system. The proposed scheme can not only guarantee the impact of continuous‐time uncertainties and discrete‐time sampled estimation errors on the faulty system to satisfy a H _∞ performance index but also repress the negative effect of the unknown intersample behavior of continuous‐time fault by use of an inequality technique. Finally, simulation results are included to demonstrate the feasibility of the proposed method. Copyright © 2014 John Wiley & Sons, Ltd.     

Recursive least squares estimation methods for a class of nonlinear systems based on non-uniform sampling

Many dynamic processes in practice have nonlinear characteristics and must be described by using nonlinear models. It remains to be a challenging problem to build the models of such nonlinear systems and to estimate their parameters. This article studies the parameter estimation problem for a class of Hammerstein-Wiener nonlinear systems based on non-uniform sampling. By means of the auxiliary model identification idea, an auxiliary model-based recursive least squares algorithm is derived for the systems. In order to enhance the computational efficiency, an auxiliary model-based hierarchical least squares algorithm is proposed by utilizing the hierarchical identification principle. The simulation results confirm the effectiveness of the proposed algorithms.     

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.     

Adaptive sampling strategy for characterizing spatial distribution of soil liquefaction potential using cone penetration test

Characterizing spatial distribution of soil liquefaction potential is critical for assessing liquefaction-related hazards (e.g. building damages caused by liquefaction-induced differential settlement). However, in engineering practice, soil liquefaction potential is usually measured at limited locations in a specific site using in situ tests, e.g. cone penetration tests (CPTs), due to the restrictions of time, cost and access to subsurface space. In these cases, liquefaction potential of soil at untested locations requires to be interpreted from limited measured data points using proper interpolation method, leading to remarkable statistical uncertainty in liquefaction assessment. This underlines an important question of how to optimize the locations of CPT soundings and determine the minimum number of CPTs for achieving a target reliability level of liquefaction assessment. To tackle this issue, this study proposes a smart sampling strategy for determining the minimum number of CPTs and their optimal locations in a self-adaptive and data-driven manner. The proposed sampling strategy leverages on information entropy and Bayesian compressive sampling (BCS). Both simulated and real CPT data are used to demonstrate the proposed method. Illustrative examples indicate that the proposed method can adaptively and sequentially select the required number and optimal locations of CPTs.     

An adaptive Kriging method with double sampling criteria applied to hydrogen preparation case

Kriging model has been widely used to approximate expensive black-box problems in many engineering design fields. How to choose an appropriate sampling strategy to produce new expensive updated points is crucial. For this purpose, an adaptive Kriging method with double sampling criteria (AKM-DSC) is proposed. During every iteration of it, maximum curvature criterion and maximum variance criterion based on Kriging model are respectively optimized by trust region (TR) strategy to produce two candidate points. And then, a new screening method is used to determine final expensive-evaluation points from the two candidates. The proposed method is compared with the two typical Kriging modeling methods. The comparison results of seventeen benchmark functions verify that the proposed method can generate higher accuracy Kriging model. Finally, a hydrogen preparation case illustrates the engineering application value of the AKM-DSC method.