基于触发器机制的主动数据库模型研究

触发器是当前商用DBMS用于完整性实施和主动控制的一个主要机制。为了满足许多应用系统对数据库系统的主动性需求,提出了一种基于触发器机制的主动数据库的新模型,并且在此模型基础之上,运用了一种简单的、易于实现的方法来建立基于触发器机制的主动数据库。     

Model-based and Fuzzy Logic Approaches to Condition Monitoring of Operational Wind Turbines

 It is common for wind turbines to be installed in remote locations on land or offshore, leading to difficulties in routine inspection and maintenance. Further, wind turbines in these locations are often subject to harsh operating conditions. These challenges mean there is a requirement for a high degree of maintenance. The data generated by monitoring systems can be used to obtain models of wind turbines operating under different conditions, and hence predict output signals based on known inputs. A model-based condition monitoring system can be implemented by comparing output data obtained from operational turbines with those predicted by the models, so as to detect changes that could be due to the presence of faults. This paper discusses several techniques for model-based condition monitoring systems: linear models, artificial neural networks, and state dependent parameter "pseudo" transfer functions. The models are identified using supervisory control and data acquisition (SCADA) data acquired from an operational wind firm. It is found that the multiple-input single-output state dependent parameter method outperforms both multivariate linear and artificial neural network-based approaches. Subsequently, state dependent parameter models are used to develop adaptive thresholds for critical output signals. In order to provide an early warning of a developing fault, it is necessary to interpret the amount by which the threshold is exceeded, together with the period of time over which this occurs. In this regard, a fuzzy logic-based inference system is proposed and demonstrated to be practically feasible.     

基于CRF和规则相结合的地理命名实体识别方法

为了识别文本中海量的地理命名信息,以CRF(条件随机场)模型识别为基础,加入制定的规则,来提高CRF模型识别的召回率,从而提高整体的地理命名实体识别效果。通过选取适合的地理命名实体识别的特征模板,验证特征的有效性以及分析CRF模型识别结果中的未识别实体样本,设计针对未识别实体的规则用以修正识别结果。实验表明,对地名和组织名结合规则进行修正后的F值达到了91.61%和85.74%,有了显著提高。     

基于核电管道评估系统的硬件与故障诊断设计

以核电管道评估系统为对象,对其核心部分硬件系统的故障诊断与控制设计进行了研究。利用程控放大、幻象供电、故障诊断等电路设计了硬件调理模块。通过Keil软件对STM32芯片嵌入式软件编程的方式实现了硬件系统的控制与故障诊断功能,并由设计的串口通信协议经RS232与软件系统通讯。通过Labview软件开发了硬件自动化测试平台和故障诊断监测程序,保证硬件系统可靠性的同时提升了测试效率并减少了人力成本。设计的硬件控制与故障诊断模块满足要求,已成功应用在出口国外某核电厂的管道评估系统中,为核电厂安全经济可靠地运行提供了保障。     

一种基于目标约束遗传算法的地层对比方法

针对油田开发钻井剖面地层对比的具体特点和实际问题应满足的约束条件,提出了一种基于目标约束遗传算法的地层对比方法。该方法既能较好地完成常规地层对比,也可对因断层或地层尖灭等地质现象造成的地层缺失、错位等复杂情况进行处理。该方法具有较好的鲁棒性,随机搜索能力强,对复杂情况下的地层对比具有较好的适应性。给出了具体的实现算法,对实际资料的处理结果证明了方法的有效性。     

A novel diagnostic and prognostic framework for incipient fault detection and remaining service life prediction with application to industrial rotating machines

Data-driven machine health monitoring systems (MHMS) have been widely investigated and applied in the field of machine diagnostics and prognostics with the aim of realizing predictive maintenance. It involves using data to identify early warnings that indicate potential system malfunctioning, predict when system failure might occur, and pre-emptively service equipment to avoid unscheduled downtime. One of the most critical aspects of data-driven MHMS is the provision of incipient fault diagnosis and prognosis regarding the system’s future working conditions. In this work, a novel diagnostic and prognostic framework is proposed to detect incipient faults and estimate remaining service life (RSL) of rotating machinery. In the proposed framework, a novel canonical variate analysis (CVA)-based monitoring index, which takes into account the distinctions between past and future canonical variables, is employed for carrying out incipient fault diagnosis. By incorporating the exponentially weighted moving average (EWMA) technique, a novel fault identification approach based on Pearson correlation analysis is presented and utilized to identify the influential variables that are most likely associated with the fault. Moreover, an enhanced metabolism grey forecasting model (MGFM) approach is developed for RSL prediction. Particle filter (PF) is employed to modify the traditional grey forecasting model for improving its prediction performance. The enhanced MGFM approach is designed to address two generic issues namely dealing with scarce data and quantifying the uncertainty of RSL in a probabilistic form, which are often encountered in the prognostics of safety-critical and complex assets. The proposed CVA-based index is validated on slowly evolving faults in a continuous stirred tank reactor (CSTR) system, and the effectiveness of the proposed integrated diagnostic and prognostic method for the monitoring of rotating machinery is demonstrated for slow involving faults in two case studies of an operational industrial centrifugal pump and one case study of an operational centrifugal compressor.     

Two- or three-stage least squares?

Two elements enter the choice between 2 and 3SLS for full-system estimation: statistical efficiency and computational cost. 2SLS always has the computational edge, but 3SLS can be more efficient, a relative advantage that increases with the strength of the interrelations among the error terms. A measure of these interrelations is thus helpful in making the choice, and, when there are only two equations, this has suggested using a high pairwise error correlation as an indicator of when to use 3SLS. In larger systems of equations, however, these pairwise correlations can remain small even though more general interrelations give 3SLS the relative advantage. More general indicators are therefore needed, and this paper suggests three such and demonstrates their efficacy.Professor of Economics, Boston College, and Principal Research Associate, Center for Computational Research in Economics and Management Science, MIT. All computation was done on the TROLL system at MIT. My thanks go to Josh Charap for his able research assistance. This research was sponsored in part by the National Science Foundation under grant #IST-8420614.     

主动数据库及其实时应用支持

一、引言 传统的DBMS技术对于要求存取大量共享数据和拉制知识的应用,显得无能为.如CIM、过程控制、合作处理、战斗管理、空中交通管制、网络管理等,这类应用的一个共同求是DBMS能监视关于     

A Multi-Net System for the Fault Diagnosis of a Diesel Engine

A multi-net fault diagnosis system designed to provide an early warning of combustion-related faults in a diesel engine is presented. Two faults (a leaking exhaust valve and a leaking fuel injector nozzle) were physically induced (at separate times) in the engine. A pressure transducer was used to sense the in-cylinder pressure changes during engine cycles under both of these conditions, and during normal operation. Data corresponding to these measurements were used to train artificial neural nets to recognise the faults, and to discriminate between them and normal operation. Individually trained nets, some of which were trained on subtasks, were combined to form a multi-net system. The multi-net system is shown to be effective when compared with the performance of the component nets from which it was assembled. The system is also shown to outperform a decision-tree algorithm (C5.0), and a human expert; comparisons which show the complexity of the required discrimination. The results illustrate the improvements in performance that can come about from the effective use of both problem decomposition and redundancy in the construction of multi-net systems.     

A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art

Preventing induction motors (IMs) from failure and shutdown is important to maintain functionality of many critical loads in industry and commerce. This paper provides a comprehensive review of fault detection and diagnosis (FDD) methods targeting all the four major types of faults in IMs. Popular FDD methods published up to 2010 are briefly introduced, while the focus of the review is laid on the state-of-the-art FDD techniques after 2010, i.e. in 2011–2015 and some in 2016. Different FDD methods are introduced and classified into four categories depending on their application domains, instead of on fault types like in many other reviews, to better reveal hidden connections and similarities of different FDD methods. Detailed comparisons of the reviewed papers after 2010 are given in tables for fast referring. Finally, a dedicated discussion session is provided, which presents recent developments, trends and remaining difficulties regarding to FDD of IMs, to inspire novel research ideas and new research possibilities.