Equipment failure rate updating-Bayesian estimation

The paper presents a Bayes' method for augmenting generic equipment failure data with a prior distribution - predicated on the evidence, e.g., plant data - resulting in a posterior distribution. The depth of the evidence is significant in shaping the characteristics of the posterior distribution. In conditions of insufficient data about the prior distribution or great uncertainty in the generic data sources, we may use "constrained non-informative priors". This representation of the prior preserves the mean value of the failure rate estimate and maintains a broad uncertainty range to accommodate the site-specific event data. Although the methodology and the case study presented in this paper focus on the calculation of a time-based (i.e., failures per unit time) failure rate, based on a Poisson likelihood function and the conjugate gamma distribution, a similar method applies to the calculation of demand failure rates utilizing the binomial likelihood function and its conjugate beta distribution.     

改进的四子空间方法及其在电厂设备状态监测中的应用

四子空间方法作为常用的状态监测方法,需要假设过程变量服从高斯分布,实际中大部分的工业数据并不服从高斯分布,这使得四子空间方法的应用范围非常有限.基于此,本文使用核密度估计方法来改进传统的四子空间方法,得到了适用于一般分布下的基于核密度估计的四子空间状态监测方法.最后,利用电厂高温过热器的实际数据进行检验.结果表明改进的四子空间方法更为普适,状态监测效果也有很大的提高.     

和声搜索算法优化快速消费品生产配送协调调度

研究具有生产准备环节的快速消费品生产配送问题,考虑工厂和配送中心的库存限制,工厂产能限制和劳动力限制,建立一个多周期、多工厂、多产品、多配送中心、多客户的混合整数线性规划模型,旨在最小化准备成本、生产成本、库存成本和配送成本。通过设计一种遗传和声搜索算法对模型进行求解。最后给出一个算例说明所提模型和算法的可行性和有效性。     

Evolution of Charging Programs for Achieving Required Gas Temperature Profile in a Blast Furnace

This article presents an approach by which charging programs in the blast furnace can be evolved. The core of the method is a mathematical model, which on the basis of a given charging program estimates the two-dimensional distribution of burden layers in the shaft. A gas flow model uses this information to estimate the gas distribution, applying a simplified treatment of the conditions in the upper shaft. The aim is to find the charging program that gives a state of the furnace shaft matching a target for the radial temperature profile at the level of an in-burden probe. This is accomplished by applying a genetic algorithm (GA) that makes an efficient search among the huge number of potential charging programs, executing the burden and gas flow models in the function evaluations. The method is illustrated by six cases, where targets for the gas temperature distribution are given and the GA evolves the charging sequence and the chute settings for the dumps. It is demonstrated that the algorithm efficiently can evolve charging programs which yield temperatures in agreement with the targets, which holds promise for a practical application of the method in the steel plant.     

Time between events control charts for gamma distribution

Modern and emerging techniques of technology have brought a revolution in quality inspection of products. When events in highly efficient production processes occur rarely, it requires to inspect and monitor the time between occurrence of these events (TBE). The exponential and gamma distributions are commonly used models for time between events (TBE) data. In this article, a new monitoring scheme has been established for TBE data based on exponential and gamma distributions. In a previous research, transformation-based control charts have been developed for TBE. The proposed study is aimed to use the exact probability distribution of charting statistic rather than applying transformations to data and this has remained still unaddressed. Average run length (ARL) and percentage decrease in ARL (ΔARL) have been calculated using Monte Carlo simulations and the proposed monitoring method has been compared with existing techniques applied to transformed data. The proposed scheme provides a simpler design structure and better performance on different sample sizes in identifying annoying process variations. Further, the technique has been applied to simulated and real-life data sets of time between manufacturing plant accidents to highlight the worth and particle applicability of the proposed work.     

SEM／EDS技术表征催化剂表面元素分布的应用

为帮助工厂了解活性组分在催化剂载体表面的分布情况，利用扫描电子显微镜和X-射线能量色散谱（SEM—EDS）进行测试分析，结果显示，500—6／5000—12测试法能简单有效地对催化剂上元素的分布情况进行表征，对催化剂表面上活性组分的分布研究具有重要研究价值。     

Establishment and Analysis of the Fault Tree for the Oil Transfer Pump System in CNPC Work Zone

The reliability of the equipment is very important for the large petrochemical industry, especially for oil pump as the core component of driving equipment. In order to reduce the loss of the enterprise brought by equipment failure, it is need to find those reasons which may lead to equipment failure and take some preventive measures as early as possible. This article analyzes the failure of the oil transfer pump system in CNPC work zone systematically, qualitatively and quantitatively, using the fault tree analysis method. Then 105 groups of minimal cut sets are found, and the probability of system failure after a certain time operation is calculated by using Weibull distribution. Combined with specific requirements of reliability, the work zone may make a scientific decision of plant maintenance cycle according to the conclusion.     

Common cause failure prediction using data mapping

To estimate power plant reliability, a probabilistic safety assessment might combine failure data from various sites. Because dependent failures are a critical concern in the nuclear industry, combining failure data from component groups of different sizes is a challenging problem. One procedure, called data mapping, translates failure data across component group sizes. This includes common cause failures, which are simultaneous failure events of two or more components in a group. In this paper, we present a framework for predicting future plant reliability using mapped common cause failure data. The prediction technique is motivated by discrete failure data from emergency diesel generators at US plants. The underlying failure distributions are based on homogeneous Poisson processes. Both Bayesian and frequentist prediction methods are presented, and if non-informative prior distributions are applied, the upper prediction bounds for the generators are the same.     

阻尼矩阵选取对核电厂结构地震响应的影响分析

已有研究表明时程动力法中阻尼矩阵的确定对复杂结构工程的地震响应有明显的影响,而核电厂安全相关结构在抗震分析时通常采用时程动力法。为研究阻尼矩阵的选取对核电厂结构的地震响应的影响,从比例阻尼拟合的精度出发,基于结构不同振型对结构产生的影响程度入手,建立更为精确的结构阻尼模型,即等效比例阻尼,并进一步考虑结构自振特性以加权最小二乘法拟合确定等效比例阻尼系数,提出了核电厂结构地震响应分析相应的计算方法。计算结果表明,该方法较传统比例阻尼的方法更接近精确解,是核电工程抗震分析的一个实用参考应用。     

Two-stage Bayesian models—application to ZEDB project

A well-known mathematical tool to analyze plant specific reliability data for nuclear power facilities is the two-stage Bayesian model. Such two-stage Bayesian models are standard practice nowadays, for example in the German ZEDB project or in the Swedish T-Book, although they may differ in their mathematical models and software implementation. In this paper, we review the mathematical model, its underlying assumptions and supporting arguments. Reasonable conditional assumptions are made to yield tractable and mathematically valid form for the failure rate at plant of interest, given failures and operational times at other plants in the population. The posterior probability of failure rate at plant of interest is sensitive to the choice of hyperprior parameters since the effect of hyperprior distribution will never be dominated by the effect of observation. The methods of Pörn and Jeffrey for choosing distributions over hyperparameters are discussed. Furthermore, we will perform verification tasks associated with the theoretical model presented in this paper. The present software implementation produces good agreement with ZEDB results for various prior distributions. The difference between our results and those of ZEDB reflect differences that may arise from numerical implementation, as that would use different step size and truncation bounds.     

一种L系统与IFS相互融合的植物模拟方法

提出了一种L系统与IFS相互融合的植物形态模拟新方法。利用L系统进行植物生长规律和拓扑结构的控制和模拟,由IFS方法模拟植物具有纹理的各组成部分,由此将两种方法的优点结合在一起。此外,为便于该融合方法的具体实现,还对传统L系统的实现算法进行了改进。模拟实验结果表明:提出的方法可以方便、自由地模拟各种植物的形态,且具有更加自然与逼真的效果。     

Lifetime test evaluation with mathcad®: Automated weibull parameter and plotting point estimate with arbitrary confidence interval and graphics

This application note describes a computer-based method for automated evaluation of Weibull-distributed life-time data. It uses comercially available software, MathCad, to set up equations and graphs. The computer application is a simple and non-expensive way of evaluating and displaying your life-time data taking into consideration both parametric and non-parametric confidence intervals. It is valid for arbitrarily censored data.     

Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm

Since the beginning of time, humans have relied on plants for food, energy, and medicine. Plants are recognized by leaf, flower, or fruit and linked to their suitable cluster. Classification methods are used to extract and select traits that are helpful in identifying a plant. In plant leaf image categorization, each plant is assigned a label according to its classification. The purpose of classifying plant leaf images is to enable farmers to recognize plants, leading to the management of plants in several aspects. This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes. This modified algorithm works on different sets of plant leaves. The proposed algorithm examines several benchmark functions with adequate performance. On ten plant leaf images, this classification method was validated. The proposed model calculates precision, recall, F-measurement, and accuracy for ten different plant leaf image datasets and compares these parameters with other existing algorithms. Based on experimental data, it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%.     

Parameters affecting the efficient delivery of mesoporous silica nanoparticle materials and gold nanorods into plant tissues by the biolistic method

Applying nanotechnology to plant science requires efficient systems for the delivery of nanoparticles (NPs) to plant cells and tissues. The presence of a cell wall in plant cells makes it challenging to extend the NP delivery methods available for animal research. In this work, research is presented which establishes an efficient NP delivery system for plant tissues using the biolistic method. It is shown that the biolistic delivery of mesoporous silica nanoparticle (MSN) materials can be improved by increasing the density of MSNs through gold plating. Additionally, a DNA-coating protocol is used based on calcium chloride and spermidine for MSN and gold nanorods to enhance the NP-mediated DNA delivery. Furthermore, the drastic improvement of NP delivery is demonstrated when the particles are combined with 0.6 μm gold particles during bombardment. The methodology described provides a system for the efficient delivery of NPs into plant cells using the biolistic method.     

Plant water-stress detection by digital imaging using the entropy concept: application to menthe plant (Mentha viridis)

Abstract

The purpose of this work is to demonstrate the capability of digital imaging within the visible range of the electromagnetic radiation to detect early signs of water stress in plants. The approach is based on colour information and on using the entropy concept. The method is composed of two main steps. The first one classifies pixels of the image that contains plant and soil into plant and background pixels. The classification is performed by a hierarchical analysis method by using a two-dimensional (2D) histogram algorithm. The second one introduces a non-destructive water stress indicator using the entropy concept. The method is applied to detect the extent of modification in menthe plants occurring under water-stress conditions. A physiological study of the water status of the plant is undertaken in order to compare the results obtained by the proposed approach. The results show that the proposed methodology is able to detect the signs of water stress in the menthe plant.     

A method for identifying instrument faults in nuclear power plants possibly leading to wrong situation assessment

In this paper, we propose a method for identifying instrument faults that could potentially affect an operators’ situation assessment capability in nuclear power plants (NPPs), an issue which has received a lot of attention recently. In the proposed method, patterns of selected plant parameter trends in selected plant states and NPP operators’ patterns of selected plant parameter trends in selected plant states are analyzed, and a comparison between the two kinds of patterns is performed to identify instrument faults which could potentially affect a NPP operators’ capability to correctly assess a plant's conditions. An example application is presented to demonstrate how the proposed method can be used to identify the possibilities of operators’ developing a wrong situation assessment because of instrument faults and to identify the corresponding safety concerns. We conclude that in order to get more accurate results, an analysis with a full-scope NPP simulator and interviews with NPP operators will be necessary.     

In vivo nanoelectrospray for the localization of bioactive molecules in plants by mass spectrometry

The method for the localization of bioactive molecules in plants is highly needed since it provides a fundamental prerequisite for understanding their physiological and ecological functions. Here, we propose a simple method termed in vivo nanoelectrospray for the localization of bioactive molecules in plants without sample preparation. A capillary is partly inserted into the plant to sample liquid from a highly located region, and then, a high voltage is applied to the plant to generate an electrospray from the capillary tip for mass spectrometry analysis. Using this method, bioactive molecules such as saccharides, glycoalkaloids, flavonoids, organic acids, and glucosinolates (GLs) are detected in the target regions of living plants or fresh fruits. Original information for endogenous chemicals including liable molecules in plant can be obtained. A sketchy three-dimensional distribution of glycoalkaloids in a cherry tomato has been obtained. The present work provides a powerful tool for the study of bioactive molecules in a living plant by mass spectrometry.     

Automotive reliability prediction based on early field failure warranty data

This paper provides a reliability prediction method to identify vehicle components that have the potential to become actionable items (such as a recall decision) based on their early field failure (4 or 5 months in service) warranty data. The vehicle customer mileage distribution from the warranty database is also discussed utilizing a mathematical model known as the lognormal distribution. The applicability of the above prediction method is demonstrated. © 1998 John Wiley & Sons, Ltd.     

基于声源监测的厂界噪声预测及贡献分析方法研究

噪声监测是环境噪声预测和治理的重要技术方法。提出了一种基于声强测量的声源监测方法并应用于电厂环境噪声预测和厂界噪声贡献分析。在电厂主要设备噪声源附近布置测点测量并计算设备厂房的辐射声强，将设备厂房简化为面声源建立噪声预测模型，并以测量计算的声强级作为声源模型的源强。利用该模型计算厂界预测点A声级，与实验值具有良好的一致性，验证了该声源监测方法数据的可靠性与噪声预测模型的正确性。通过该模型计算分析了电厂主要噪声源对厂界噪声排放的贡献和影响，为电厂噪声治理提供技术依据。     

快速评估运行空分设备实际性能的方法分析

阐述一种简捷有效的空分设备运行性能评估方法。以采用常规内压缩流程的60000 m3/h空分设备为例,总结采用快速评估方法时空分设备参数的修正项和修正公式,介绍快速评估方法的具体实施和应用效果。