基于退化数据与marker数据综合的产品可靠性建模分析

基于性能退化数据的可靠性建模分析方法,为现代工业中长寿命、高可靠产品的可靠性研究提供了重要途径,但在破坏性测量中无法获得足够的产品性能退化数据.对此,综合利用产品性能退化数据和marker数据对产品可靠性进行建模,不仅能够提高可靠性评估的精度,还可以在产品运行过程中通过对marker的测量来预测产品剩余寿命,从而为产品维修、更换以及备件决策提供依据.采用二元Wiener过程对产品的性能参数和marker进行建模,给出模型参数的估计方法和基于marker测量数据的剩余寿命预测方法,并通过仿真示例验证所提出方法的有效性.     

系统健康管理中的寿命模型仿真研究

在航空、航天、通信等领域,高可靠性和长寿命设计的产品所占比重逐渐增大;性能退化状态评估和剩余使用寿命预测技术在提高该类产品安全性和维护效率、降低全寿命周期成本等方面意义重大;针对当前国内健康管理研究中缺乏寿命及可靠性基础数据的现状,介绍了系统寿命预测的典型过程,重点分析了寿命模型研究和仿真中存在的若干问题,旨在建立实现简单且与实际系统运行过程相似度高的寿命模型的建模方法,为后续系统性能状态评估提供有效的数据支持。     

电子元器件加速退化寿命评估方法研究

基于退化轨迹的评估方法是退化型产品进行可靠性评估的主要方法,适合于对具有退化失效机理的高可靠长寿命产品进行可靠性评估;基于退化轨迹的可靠性评估方法首先选取合适的退化轨迹模型,利用退化数据对退化轨迹进行模型拟合得到模型参数,然后根据退化轨迹外推得到伪失效寿命,最后基于伪失效寿命利用最小二乘法进行统计分析确定产品的失效分布,并通过假设检验的方法选择拟合度最优的分布;本文以大功率开关的加速退化试验数据为例进行了分析和说明.     

大数据结构化与数据驱动的复杂系统维修决策

现代大型机电系统组成结构越来越复杂、智能化程度越来越高, 然而系统维修工作却越来越困难; 另外, 尽管快速发展的信息技术使得系统内部的各种流数据得到了有效的保存, 但却缺乏对这类大数据的有效利用、实现复杂系统的维修控制与决策.为此, 提出了大数据结构化与数据驱动的复杂系统维修决策方法.大数据结构化使用了层次分析法(Analytic hierarchy process, AHP)的思想, 依次建立系统维修的各个层级模型; 基于模型抽象出支持系统维修的数据变量、提炼出各层级变量的表达函数; 研究进一步实现了维护决策的数据驱动技术, 在模型和函数之上定义了数据状态块矩阵, 通过设计矩阵的特殊运算算法完成维修决策的数据驱动.最后, 使用一个具体的例子来说明提出方法的可用性, 结果证明提出的方法是可行的, 符合设备维修决策建设目标, 即维修方法经济、高效与实用.     

大数据下数模联动的随机退化设备剩余寿命预测技术

面向大数据背景下随机退化设备剩余寿命(Remaining useful life, RUL)预测的现实需求, 结合随机退化设备监测大数据特点及剩余寿命预测不确定性量化这一核心问题, 深入分析了机理模型与数据混合驱动的剩余寿命预测技术、基于机器学习的剩余寿命预测技术、统计数据驱动的剩余寿命预测技术以及机器学习和统计数据驱动相结合的剩余寿命预测技术的基本研究思想和发展动态, 剖析了当前研究存在的局限性和共性难题. 针对存在的局限性和共性难题, 以多源传感监测大数据下剩余寿命预测问题为例, 提出了一种数模联动的大数据下随机退化设备剩余寿命预测解决思路, 并通过航空发动机多源监测数据初步验证了该思路的可行性和有效性. 最后, 借鉴数模联动思路, 综合考虑机器学习方法和统计数据驱动方法的优势, 紧紧扭住大数据背景下随机退化设备剩余寿命预测不确定性量化问题, 提出了大数据背景下深度学习与随机退化建模交互联动、监测大数据与剩余寿命及其预测不确定性映射机制、非理想大数据下的剩余寿命预测等亟待解决的关键科学问题.     

基于数据驱动的小子样复杂系统剩余使用寿命预测方法综述及展望

针对小子样背景下复杂系统剩余使用寿命(RUL)预测的工程需求,结合复杂系统失效的时间数据、监测数据特点和RUL预测的不确定性问题,综述了小子样数据驱动的复杂系统RUL的预测方法。在小子样数据驱动的寿命预测技术中,数据的真实性、连续性和完整性等问题成为制约RUL预测准确度的重要因素。深入分析了基于失效时间数据、性能退化数据和多源数据融合的RUL预测技术的基本研究方法和发展动态,最后探讨了RUL预测领域未来可能的研究方向。     

基于比例故障率模型的转向架牵引电机滚动轴承可靠性评估

为保障轨道车辆转向架牵引电机滚动轴承安全、平稳、可靠地运行,提出一种基于振动特征的轨道车辆转向架牵引电机滚动轴承(以下部分简称电机轴承)可靠性评估方法.该方法首先从实测得到的振动信号中提取并优选出能反映电机轴承状态的特征指标;然后,将优选出的敏感特征指标利用主成分分析法(PCA)进行多域特征指标的加权融合,得到一个能全面、准确、有效地表征电机轴承性能状态的特征指标;再将这个融合后的特征指标作为响应协变量与可靠度之间建立可靠性评估模型,从而对电机轴承运行可靠性提供了有效的评估.最后通过在某车辆转向架牵引电机轴承上的实际应用说明该方法的有效性和合理性.     

双时间尺度下的设备随机退化建模与剩余寿命预测方法

基于退化建模的剩余寿命预测（Remaining useful life,RUL）是当前可靠性领域研究的热点.现有的退化模型都是针对单个时间尺度下的退化设备,缺少对设备性能变化与多个时间尺度相关的退化建模与剩余寿命预测方法.鉴于此,本文基于Wiener过程提出了一种双时间尺度随机退化建模与剩余寿命预测方法,用随机比例系数描述不同时间尺度之间的不确定关系,推导出丫首达时间意义下设备的双时间尺度剩余寿命分布,讨论了其与基于单时间尺度退化模型得到的剩余寿命分布之间的关系,并给出了基于历史退化数据的未知参数极大似然估计方法.最后,将所提方法应用到惯性平台关键器件陀螺仪的退化建模与剩余寿命预测中,验证了方法的有效性.     

模型未知LTI系统的数据驱动预测控制

针对含有随机噪声的模型未知线性时不变(LTI,linear time invariant)系统模型建立过程复杂且控制律难以得到的问题,提出一种基于数据驱动的预测控制方法;基于系统行为学理论和平衡子系统辨识方法,仅利用测量得到的系统数据构建被控系统的非参数模型,将其和预测控制理论相结合设计出基于数据驱动的预测控制器,对于系统测量数据中存在的有界加性高斯噪声,通过引入数据的松弛变量和L2正则项来降低噪声扰动的影响,采用滚动时域优化策略计算最优控制序列并将其作用于被控系统,实现了系统对设定值的轨迹跟踪;将所提控制策略应用于四容水箱系统,仿真结果表明所提方法能实现四容水箱系统的液位跟踪控制,且与同样基于数据驱动的子空间预测控制方案相比,所提方法具有更好的动态性能,且该策略在抗噪声扰动方面有明显优势,具有更强的鲁棒性。     

基于监控日志的航班查询系统可靠性评估方法

针对运行阶段的系统软件可靠性评估理论和评估方法的研究缺失,提出一种基于系统监控日志的软件可靠性评估方法。以中国航信的航班查询系统为可靠性评估研究对象,首先解析该系统的监控日志,提出基于监控日志的失效数据提取方法。其次,基于航班查询系统的特点和可靠性数据的分析结果,构建了航班查询系统可靠性模型。最后,实验验证了该可靠性模型是可行的,并利用此模型评估了航班查询系统的可靠性,形成了系统软件可靠性评估过程方法。     

Decision tree-based technology credit scoring for start-up firms: Korean case

Various types of Technology Credit Guarantees (TCGs) have been issued to support technology development of start-up firms. Technology evaluation has become a critical part of TCG system. However, general technology credit scoring models have not been applied reflecting the special phenomena of start-ups, which are distinguishable from those of established firms. Furthermore, somewhat complicated approaches have been applied to existing models. We propose a rather simple decision tree-based technology credit scoring for start-ups which can serve as a-replacement for the complicated models currently used for general purposes. Our result is expected to provide valuable information to evaluator for start-up firms.     

Review of urban storm water models

This paper reviews models for simulating storm water quantity and quality in an urban environment. This has been achieved by examining a number of storm water models in current use. The important features of twelve models, which represent a wide range of capabilities and spatial and temporal resolution have been described. Specific topics covered are: identifying important urban water quality parameters; the classification of modelling approaches; modelling approaches used to estimate water quantity and quality. These include statistical, empirical, hydraulic and hydrological models. Water resources management and planning tools, that are included in some urban storm water models, such as economic analysis, optimisation and risk analysis are also discussed. Features of twelve storm water models have been summarised. These models have been chosen because they demonstrate how components that are important in managing urban storm water have been incorporated in a modelling framework. These models have been categorised in terms of their functionality, accessibility, water quantity and quality components included in the model and their temporal and spatial scale. The information in this paper provides planners and managers with an overview of modelling approaches that have been used to simulate storm water quantity and quality. In particular, it provides managers with an appreciation of the limitations and assumptions made in various modelling approaches. This review will also benefit modellers by providing a comprehensive summary of approaches and capabilities of a number of storm water models in current use. Potential urban storm water research opportunities have also been identified.     

基于模型驱动架构的数据库重构

针对交叉使用数据库重构方法较为困难的问题,提出一种基于模型驱动架构（MDA）的通用数据库重构框架,其中包括数据库重构中的元模型定义和模型转换定义,并采用EMF和Operational QVT构建工具,该框架利用MDA的模型转换能力实现了数据库重构的自动化。     

A review on queueing network models with finite capacity queues for software architectures performance prediction

A review is carried out on how queueing network models with blocking have been applied so far into the performance evaluation and prediction of Software Architectures (SA). Queueing network models with finite capacity queues and blocking have recently been introduced and applied as more realistic models of systems with finite capacity resources and population constraints. Queueing network models have been often adopted as models for the evaluation of software performance. Starting from our own experience, we observe the need of a more accurate definition of the performance models of SA to capture some features of the communication systems. We consider queueing networks with finite capacity and blocking after service (BAS) to represent some synchronization constraints that cannot be easily modeled with queueing network models with infinite capacity queues. We investigate the use of queueing networks with blocking as performance models of SA with concurrent components and synchronous communication. Queueing theoretic analysis is used to solve the queueing network model and study the synchronous communication and performance of concurrent software components. Our experience is supported by other approaches that also propose the use of queueing networks with blocking. Directions for future research work in the field are included.     

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex for conventional statistical techniques to process quickly and efficiently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes metaheuristic optimisation algorithms (also known as nature inspired algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these, as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed.     

An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem

The vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem, and many models and algorithms have been proposed to solve the VRP and its variants. Although existing approaches have contributed significantly to the development of this field, these approaches either are limited in problem size or need manual intervention in choosing parameters. To solve these difficulties, many studies have considered learning-based optimization (LBO) algorithms to solve the VRP. This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches. We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms. Finally, we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms.      

On the Theories and Numerics of Continuum Models for Adaptation Processes in Biological Tissues

Computational continuum mechanics have been used for a long time to deal with the mechanics of materials. During the last decades researches have been using many of the theoretical models and numerical approaches of classical materials to deal with biological tissue which, in many senses, are a much more sophisticated material. We aim to review the last achievements of continuum models and numerical approaches on adaptation processes in biological tissues. In this review, we are looking, in particular, at growth in terms of changes of density and/or volume as, e.g., in collagen remodeling, wound healing, arterial thickening, etc. Furthermore, we point out some of the most relevant limitations of the current state-of-the-art in terms of these well established computational continuum models. In connection with these limitations, we will finish by discussing the trend lines of future work in the field of modeling biological adaptation, focusing on the computational approaches and mechanics that could overcome the current drawbacks. We would also like to attract the attention of all those researchers in classical materials (metal, alloys, composites, etc), to point out how similar the continuum and computational models between our fields are. We hope we can motivate them for getting their expertize in this challenging field of research.     

Cloud service selection using cloud service brokers: approaches and challenges

Cloud computing users are faced with a wide variety of services to choose from. Consequently, a number of cloud service brokers (CSBs) have emerged to help users in their service selection process. This paper reviews the recent approaches that have been introduced and used for cloud service brokerage and discusses their challenges accordingly. We propose a set of attributes for a CSB to be considered effective. DifFerent CSBs' approaches are classified as either single service or multiple service models. The CSBs are then assessed, analyzed, and compared with respect to the proposed set of attributes. Based on our studies, CSBs with multiple service models that support more of the proposed effective CSB attributes have wider application in cloud computing environments.     

On one approach to constructing timetables of freight trains in a railroad network

We consider scheduling problems for routing freight trains through a railroad network. To solve these problems, we propose models and methods that use ideas and principles previously applied to construct part processing job schedules in industrial shops. The proposed approaches have been implemented in software, and our computational experiments have shown their efficiency.