Internal leakage detection for feedwater heaters in power plants using neural networks

As interest in safety and performance of power plants becomes more serious and wide-ranging, the significance of research on turbine cycles has attracted more attention. This paper particularly focuses on thermal performance analysis under the conditions of internal leakages inside closed-type feedwater heaters (FWHs) and their diagnosis to identify the locations and to quantify leak rates. Internal leakage is regarded as flow movement through the isolated path but remaining inside the system boundary of a turbine cycle. For instance, leakages through the cracked tubes, tube-sheets, or pass partition plates in a FWH are internal leakages. Internal leakages impact not only plant efficiency, but also direct costs and/or even plant safety associated with the appropriate repairs. Some types of internal leakages are usually critical to get the parts fixed and back in a timely manner. The FWHs installed in a Korean standard nuclear power plant were investigated in this study. Three technical steps have been, then, conducted: (1) the detailed modeling of FWHs covering the leakage from tubes, tube-sheets, or pass partition plates using the simulation model, (2) thermal performance analysis under various leakage conditions, and (3) the development of a diagnosis model using a feed-forward neural network, which is the correlation between thermal performance indices and leakage conditions. Since the operational characteristics of FWHs are coupled with one another and/or with other neighbor components such as turbines or condensers, recognizing internal leakages is difficult with only an analytical model and instrumentation at the inlet and outlet of tube- and shell-sides. The proposed neural network-based correlation was successfully validated for test cases.     

Hybrid modelling and control of the common rail injection system

We present an industrial case study in automotive control of significant complexity: the new common-rail fuel-injection system for Diesel engines under development at Magneti Marelli Powertrain. In this system, an inlet metering valve, inserted before the high pressure (HP) pump, regulates the fuel flow that supplies the common rail according to the engine operating point (e.g., engine speed and desired torque). The standard approach in automotive control based on a mean-value model for the plant does not provide a satisfactory solution as the discrete-continuous interactions in the fuel injection system, due to the slow time-varying frequency of the HP pump cycles and the fast sampling frequency of sensing and actuation, play a fundamental role. We present a design approach based on a hybrid model of the Magneti Marelli Powertrain common-rail fuel-injection system for four-cylinder multi-jet engines and a hybrid approach to the design of a rail pressure controller. The hybrid controller performs significantly better when compared with the classical mean-value based approach.     

飞机燃油箱无油空间氧气浓度测量系统研究和发展综述

为了降低飞机燃油箱可燃性,绝大部分民用和军用飞机都安装了油箱惰化系统,来控制油箱无油空间的氧气浓度,但却一直没有适用于机载燃油箱的氧浓度实时监测系统。根据多型机油箱惰化系统试飞经验,总结了基于电化学原理和光谱吸收原理测量系统使用中的问题。依据文献分析,认为基于荧光猝灭原理的传感器将成为下一代机载油箱氧浓度测量系统的核心,并详细介绍了荧光猝灭效应的原理、检测方法、受感部和测量系统框架的研究现状。最后,指出测量系统应该向着轻量化、智能化和高可靠性方向发展。     

两种固体氧化物燃料电池系统的故障建模与仿真对比研究

固体氧化物燃料电池(SOFC)系统的建模方式较多,而基于机理模型的故障诊断是能够通过系统的动态趋势辨别故障的有效手段之一,但该方法对机理模型的准确性有要求.此外,不同的燃料供给系统采用的系统结构也是有差异的,进而导致在相同故障下SOFC系统的动态响应也是不同的.因此,本文基于两种燃料供应方式,提出了分别以纯氢气和天然气作为燃料的SOFC系统结构,并基于原有机理知识进行MATLAB/Simulink系统建模.经与真实SOFC系统实验对比,搭建的系统模型能够有效模拟系统在无故障状态下的动态变化;另外,在无故障模型的基础上,分别加入两类常见故障,其一为风机故障,其二为燃料供应管路泄露.最后通过仿真分析,明确了所搭建模型的合理性,且发现了两种燃料供应对SOFC系统热响应特性是不同的,对SOFC系统故障的检测和设备选型具有重要意义.     

微通道重整器的动态特性建模与仿真

燃料电池通常需要根据负载的变化而调整工作状态,因此其动态特性是关系到系统性能的关键因素.而重整器作为燃料电池系统的一个重要部件,其动态特性的研究显然就十分必要.选用结构紧凑、性能优良的微通道重整器,通过机理建模得到其集总参数动态数学模型,然后在Matlab平台上构建了其仿真模型.根据燃料电池实际运行过程中负荷变化的特点,分别对重整器的主要调控参数,如流量、温度和压力在发生动态变化的情况下进行了动态响应的仿真.仿真结果与参考文献结论一致,表明该模型能预估重整器动态特性,从而为重整器乃至燃料电池的控制系统设计提供参考.     

变压器漏磁热损的光纤Bragg光栅检测与温升特性分析

变压器的漏磁场强度随着变压器容量的增大而增大,漏磁场强度越大在变压器各结构中引起的漏磁损耗越大,导致变压器运行效率低下,进而影响变压器的正常运行。对35 kV变压器进行试验,将漏磁产生最大处的温度与油箱产生顶端处的温度进行了对比,试验得出当开风机时该处与油箱顶端处的最高温度差为8.7℃、当负荷降至1倍功率时温差为2.9℃,它们的实时温度曲线图与变压器的运行一致,产生了局部温升的现象。通过光纤Bragg光栅（ FBG）检测温度的变化反映变压器漏磁的情况,从而实现了对变压器漏磁的实时在线监测。     

Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks

Leaks are present to some extent in all water-distribution systems. This paper proposes a leakage localisation method based on the pressure measurements and pressure sensitivity analysis of nodes in a network. The sensitivity analysis using analytical tools is not a trivial job in a real network because of the huge non-explicit non-linear systems of equations that describe its dynamics. Simulations of the network in the presence and the absence of leakage may provide an approximation of this sensitivity. This matrix is binarised using a threshold independent of the node. The binary matrix is assumed as a signature matrix for leakages. However, there is a trade-off between the resolution of the leakage isolation procedure and the number of available pressure sensors. In order to maximise the isolability with a reasonable number of sensors, an optimal sensor placement methodology, based on genetic algorithms, is also proposed. These methodologies have been applied to the Barcelona Network using PICCOLO simulator. The sensor placement and the leakage detection and localisation methodologies are applied to several district management areas (DMA) in simulation and in reality.     

基于自联想核回归的带离群值化工过程故障检测EI北大核心CSCD

基于数据驱动的故障检测模型通常要求训练数据必须是正常操作条件下的测量值.然而在实际工业生产过程中,即使在正常工况下,数据集中也难以避免存在离群值.此时若仍采用传统的基于多元统计分析的方法,其监测模型的控制限会受到严重影响,造成故障漏报.因此,为了确保当训练数据包含离群值时,监测模型仍然呈现较好的故障检测效果,本文提出了一种基于自联想核回归的故障检测方法.首先基于最小化β散度的鲁棒预白化算法对训练集进行白化计算,消除变量之间相关性对样本相似度度量的影响.然后通过自联想核回归算法重构正常工况下的验证数据,根据重构误差建立模型监测指标.为了消除离群值对故障样本重构的影响,构造截断函数来避免离群样本参与相似故障数据的重构,并对所有参与构建Q统计量的残差变量基于指数加权滑动平均方法自适应加权,得到新的监测统计量.将该方法运用于田纳西–伊斯曼过程并与其他方法进行比较,验证了本文所提故障检测算法的有效性.     

一种基于输出反馈的显式实时优化控制方法

针对一阶最优性必要条件跟踪法优化间隔较长的缺点, 提出一种基于输出反馈的显式实时优化方法. 对系统进行不同工况下的离线优化, 经函数拟合得到最优控制输入与输出变量的显式回归模型, 直接应用于实时优化, 避免了在线梯度估计. 研究一个连续搅拌釜式反应器的反应过程, 并对比两种方法的优化效果, 结果验证了所提出方法的实际使用效果

     

微型燃机燃料压力模糊PID控制的研究

微型燃气轮机的燃料压力系统是燃气轮机的重要组成部分,针对燃气轮机正常工作时燃料储罐里面的燃料压力应当恒定的要求,提出了模糊PID控制方法。在调试初期,燃料压力控制采用传统的PID进行控制和调节。由于存在干扰,控制效果不很理想。在传统的PID控制下加入模糊控制,使其PID参数有在线自整定功能。通过Matlab仿真表明,该方法比传统的PID控制效果有明显改善。     

A simulation of aircraft fuel management system

Aircrafts usually have several fuel tanks, and there are fuel transfers among these tanks along a flight. These transfers are controlled with valves, and may follow several alternative paths, since structural fuel system redundancies are provided for evident reasons. An on board program for the management and reconfiguration of the fuel system must be developed and tested. The article introduces an aircraft fuel management system simulation, which provides a platform for the study of the fuel system logic and sequencing that the on board program must implement for normal flights and for malfunction cases. The simulation environment can be easily modified and extended, for instance to consider the use of new components. A specific example is considered: an aircraft with six tanks in the wings and a tail tank. The article presents a two-layer model, the use of the model for simulation experiments, and some illustrative examples.     

多源融合信息泄漏检测方法

密码设备的侧信息泄漏检测是侧信道分析中的重要技术环节,旨在客观地评估密码设备的物理安全性。不同类型的侧信息在密码设备运行过程中同时存在,仅仅从单源侧信息的视角进行信息泄漏检测难以全面反映密码设备的真实泄漏威胁情况。因此,发展基于多源侧信息的融合信息泄漏检测方法,建立综合利用多源侧信息的泄漏检测方法体系,以期实现对密码设备物理安全性更全面、更客观地评估,是一种现实技术需求。本文基于如何融合利用多个信道的侧信息提出了3种多源融合信息泄漏检测方案：多源简单融合信息泄漏检测、多源时频融合信息泄漏检测以及基于多元T检验的多源信息泄漏检测。其中,多源简单融合信息泄漏检测在时域上组合利用多个信道的侧信息;多源时频融合信息泄漏检测综合利用了多个信道侧信息的时域信息及频域信息;基于多元T检验的多源信息泄漏检测基于多元假设检验方案构造。对比单源的信息泄漏检测方案,模拟实验和真实实验结果表明本文提出的多源融合信息泄漏检测方案可以降低检测出泄漏所需的侧信息数量,提高泄漏检测的效率。     

Skill Memory in Biped Locomotion

Robots must be able to adapt their motor behavior to unexpected situations in order to safely move among humans. A necessary step is to be able to predict failures, which result in behavior abnormalities and may cause irrecoverable damage to the robot and its surroundings, i.e. humans. In this paper we build a predictive model of sensor traces that enables early failure detection by means of a skill memory. Specifically, we propose an architecture based on a biped locomotion solution with improved robustness due to sensory feedback, and extend the concept of Associative Skill Memories (ASM) to periodic movements by introducing several mechanisms into the training workflow, such as linear interpolation and regression into a Dynamical Motion Primitive (DMP) system such that representation becomes time invariant and easily parameterizable. The failure detection mechanism applies statistical tests to determine the optimal operating conditions. Both training and failure testing were conducted on a DARwIn-OP inside a simulation environment to assess and validate the failure detection system proposed. Results show that the system performance in terms of the compromise between sensitivity and specificity is similar with and without the proposed mechanism, while achieving a significant data size reduction due to the periodic approach taken.     

柴油机共轨压力模糊自适应PID控制研究

柴油机高压共轨燃油系统中,共轨压力决定了燃油喷射压力,共轨压力随不同工况的调节能力及其压力的稳定性从根本上影响着系统性能。针对共轨压力控制,设计了模糊PID控制器,增加了积分分离与轨压预控制技术,给出了共轨压力的控制策略和实现方法。通过对PID参数的在线自适应整定,实现了在不同柴油机工况下对不同共轨压力变化的最佳控制。台架实验结果表明,共轨压力随柴油机转速与单次喷油量的增加应相应提高;当柴油机转速较高时,PID控制器应采用较大的控制参数;轨压预控制可有效地减少轨压波动和缩短轨压稳定时间;提出的控制策略和实现方法可把轨压控制偏差稳定在1.7%以下。     

Using hypothesis testing theory to evaluate principles for leakage diagnosis of automotive engines

Two different methods for diagnosing leakages in the air path of an automotive engine are investigated. The first is based on a comparison between measured and estimated air flows. The second is based on an estimation of the leakage area. The two methods are compared by using a framework of hypothesis testing and especially the power function. The investigation is made first in theory and then also on a real engine. The conclusion is that the principle based on the estimated leakage area, gives a better power function and is therefore the best choice if only leakage detection is considered. However, if also other faults need to be diagnosed, it is shown that the sensitivity to these other faults may be better with the principle based on comparison of estimated and measured air flow.     

A Methanol-Tolerant Gas-Venting Microchannel for a Microdirect Methanol Fuel Cell

As a byproduct, gas is constantly generated from the electrochemical reactions of direct methanol fuel cells (DMFCs). In the anodic channel of a DMFC, the gas forms bubbles, which leads to bubble clogging and pressure buildup if the device is miniaturized. Bubble clogging increases the flow resistance in microchannels, calling for excessive power consumption for fuel delivery. Pressure buildup aggravates the undesired crossover of methanol. In order to solve those problems, this paper introduces a gas-venting microchannel that directly removes gas bubbles from the two-phase flows of gas and methanol solution without leakage. By employing a hydrophobic nanoporous membrane, successful venting is achieved for both water and methanol fuel with a concentration of as high as 10 M. The fuel is contained without leakage under overpressures of as high as 200 kPa for both water and 10-M methanol, fulfilling the requirement of the current- as well as next-generation microdirect methanol fuel cells. A 1-D venting rate model is developed and experimentally verified for elongated bubbles. The reported bubble removal approach is also useful for other microfluidic devices, in which the accidental introduction of gas bubbles is prevalent.     

Bootstrap与模糊聚类在管道泄漏检测的研究

管道泄漏信息缺失对可靠提取小泄漏与工况变化所产生的负压波模式的特征一直是一瓶颈，也是降低系统误报警率的关键因素之一。本文利用小样本统计的Bootstrap方法，对观测样本进行模拟采样并进行统计分析，在此基础上，对管道不同工况提取特征．并用模糊聚类分析的方法分类管道小泄漏与工况变化的运行模式。仿真实验表明该方法应用在管道泄漏诊断中是简单实用和可行的。     

基于大维数据驱动的油气管网泄漏监控模糊决策方法

输油管网状态量多及工艺复杂,难以建立精确的管网数学模型,为了能够实时监控管网的安全运行情况,本文提出一种基于大维数据驱动的管网泄漏监控模糊决策方法.首先利用管网现有的数据信息,在不对数据进行降维处理的情况下,从信息物理系统的角度出发,将油气管网的拓扑结构、阀门开度等管道物理数据以及压力、流量等运行信息数据结合起来对复杂管网系统建立数据驱动模型.然后基于大维随机矩阵谱理论,将得到的信息物理数据协方差矩阵谱分布及圆环率作为模糊决策的条件对管网运行情况进行判断.当管网拓扑发生动态变化时,提出的方法可以有效地解决误报率高的问题.最后通过仿真及实例的分析,可以证明所提出方法的有效性.     

基于独立源分析的过程监测方法

多元统计过程控制要求观测数据服从正态分布，而实际的5-业过程数据大都不满足正态分布条件.独立源分析（ICA）近几年才发展起来的一种新的统计方法，可以克服对数据分布的依赖性．对此，以ICA算法为核心，引入一种新型的过程监测方法，应用ICA提取独立源，利用I^2图，Ic^2图和SPE图进行故障检测．最后以3水箱系统为例进行了实验研究，取得了很好的效果．     

Model-based fault detection and isolation for a diesel lean NOx trap aftertreatment system

The lean ${\mathrm{NO}}_x$ trap (LNT) is an aftertreatment device used to reduce nitrogen oxides emissions on Diesel engines. To operate the LNT with high conversion efficiency, an optimized regeneration schedule is required, together with closed-loop control of the air/fuel ratio during regeneration. Furthermore, to comply with emissions regulations, diagnostic schemes are needed to detect and isolate faults, typically related to aging, sulfur poisoning and thermal deactivation.The paper describes a step-by-step methodology for the design and validation of model-based fault diagnosis for a LNT aftertreatment system. The approach is based on a control-oriented model of the LNT validated with experimental data.The proposed diagnostic approach is based on the generation of residuals using system models, through the comparison of the predicted and measured values of selected output variables. The paper focuses on the detection and isolation of sensor faults and LNT parametric faults. Different diagnostic methodologies are presented in relation to the detection of specific faults.Starting from sulfur poisoning detection in a laboratory environment which represents a preliminary validation of the approach, the diagnostic scheme is extended to detect various faults under different plant configurations and operating conditions, with a final application to on-board fault detection and isolation.