Intelligent process monitoring by interfacing knowledge-based systems and multivariate statistical monitoring

An intelligent process monitoring and fault diagnosis environment has been developed by interfacing multivariate statistical process monitoring (MSPM) techniques and knowledge-based systems (KBS) for monitoring multivariable process operation. The real-time KBS developed in G2 is used with multivariate SPM methods based on canonical variate state space (CVSS) process models. Fault detection is based on T ² charts of state variables. Contribution plots in G2 are used for determining the process variables that have contributed to the out-of-control signal indicated by large T ² values, and G2 Diagnostic Assistant (GDA) is used to diagnose the source causes of abnormal process behavior. The MSPM modules developed in Matlab are linked with G2. This intelligent monitoring and diagnosis system can be used to monitor multivariable processes with autocorrelated, crosscorrelated, and collinear data. The structure of the integrated system is described and its performance is illustrated by simulation studies.     

Application of knowledge-based systems for fault diagnosis and supply restoration

A knowledge-based system (KBS) for fault diagnosis and supply restoration in an electrical power distribution system is introduced to complement the teaching of the related theory in a senior-year course. The KBS communicates with a power-system simulator which enables the setup of various scenarios. Through the KBS, the students strengthen their understanding of generic diagnostic rules, restoration algorithms, knowledge acquisition and inference mechanisms. Six training scenarios are described to illustrate how the concepts of fault diagnosis, supply restoration and various features of the KBS can be taught.     

基于WSN和数据融合技术的电力变压器故障诊断系统的研究

探讨一种基于数据融合和可拓理论的电力变压器故障诊断方法,基于此方法,应用无线传感器网络技术和.NET开发平台构建和开发出一套电力变压器故障诊断系统。该系统将WSN技术用于电力变压器油中溶解气体的无线数据采集,实现变压器油中溶解气体的实时数据采集和传送;同时运用信息融合技术对数据进行优化处理,既可以降低网络能耗又保证了数据的准确和客观;在变压器进行故障诊断时,针对现有电力变压器故障诊断的气体比值法存在的编码规则不完整、诊断故障类型单一等缺陷,使用可拓诊断方法,定量与定性相结合,使变压器故障诊断方法更为精细化。在线故障诊断可视化系统可以实现变压器油中溶解气体参数的采集、显示、分析、存储和回放等功能,实现对变压器故障信息的实时分析与自动诊断,既解决了作业人员远程实时监测变压器运行状态问题,又大大提高了变压器故障诊断的性能,为变压器在线监测和故障诊断技术提供新的发展方向。     

飞机状态监测无线传感器网络系统

ZigBee技术与无线传感器网络完美地结合在一起,成为无线传感器网络中无线通信应用的首选技术之一,具有低功耗、低成本、网络容量大、灵活安全和抗干扰能力强等特点.用ZigBee技术研制的飞机状态监测无线传感器网络系统布线少,可减少整个系统的重量和成本,增加系统的灵活性,还可快速的布局和监测数据,实时地传输动态信息,可以使飞机状态监测和故障诊断技术达到一个新水平,有力推动航空技术的发展.基于这一应用要求,将新型传感器和无线传感器网络技术相结合,设计了用于飞机状态监测的无线传感器网络系统方案及其相关节点,实验实现了系统的基本功能,初步验证了此飞机状态监测系统的可行性.     

基于ARM与ZigBee的矿用通风机监测系统设计

针对传统通风机监测模式的弊端,设计了新型矿用通风机在线监测系统.采用ARM-Linux嵌入式架构与ZigBee无线传感器网络技术实现对通风机运行状态的实时监测.无线传感器网络负责状态数据的采集与传输;采用Qt开发上位机监测软件,实时显示通风机相关部位的温度、振动、风压、风量以及电动机的电量参数,同时对通风机异常状态进行报警;通过手动与触发方式控制通风机传感器节点采集振动加速度数据,并以.txt格式存储于SD卡中以用于通风机故障诊断.整个系统安装简单、使用灵活,实际应用表明该系统能够很好地完成通风机状态监测任务.     

定值和随动单闭环系统传感器故障诊断

在线闭环系统传感器的故障, 由于控制器的调节作用, 使得其故障诊断变得复杂. 本文针对定值和随动单 闭环系统传感器故障, 在正常系统和故障系统动态特性分析基础上, 提出了一种基于系统动态趋势和数据驱动的故 障诊断方法. 该方法利用动态窗口残差数据实现故障监测; 以数据变化最大值实现故障估计; 采用二次线性回归实 现故障分离; 建立了系统故障监测、故障估计和故障分离的静态模型; 同时给出了诊断模型在线应用的标定方法和 诊断流程. 通过实验验证了方法的有效性和诊断的高精度. 该方法适用于一般的一阶定值和随动闭环控制系统传 感器的实时故障诊断.     

航空发动机传感器故障诊断设计与验证综合仿真平台

针对航空发动机传感器故障诊断算法无法快速和高效地完成硬件在回路仿真问题;提出了基于虚拟仪器语言与快速原型化技术的航空发动机传感器故障诊断硬件在回路仿真方法,并研制了基于该方法的硬件在回路实时仿真平台;经过实验验证后,该平台不仅为发动机传感器故障诊断方法提供了实时仿真平台,同时为扩展本平台使之成为完全的半物理仿真平台奠定了基础,有一定的工程实用价值。     

新型故障监测传感器在机载云台电动舵机中的应用

设计并实现了一种基于光生伏打隔离器PVI—1050NS的新型故障监测传感器,该传感器能实时监测电动舵机驱动电路中的过流、过压、过温等故障信号。通过对该故障信号的处理,可以判断驱动电路的故障位置,迅速进行定位并进行故障处理,保障了电动舵机运行的可靠性与安全性。实验结果表明:该故障监测传感器具有较高的灵敏度,大大提高了系统的可靠性。     

高速列车牵引整流器多类故障联合诊断方法

提出了一种高速列车牵引整流器多类故障联合诊断方法. 首先, 基于三电平牵引整流器开路故障分析, 建立整流器所有功率器件开路故障以及正常运行的状态空间模型并构建相应状态观测器. 然后, 基于正常状态观测器进行故障检测, 检测到故障后, 基于故障观测器区分功率器件开路故障和网侧电流传感器故障, 进而诊断出功率器件开路故障位置和网侧电流传感器故障类型. 实时仿真结果验证了本文方法的正确性和有效性.     

新能源发电设备转子安全运行监测系统设计

针对新能源（如风能、抽水蓄能等）发电设备具有运行环境恶劣、工况复杂及负载扰动大等特点,开发了新能源发电设备转子安全运行监测系统,该系统由发电设备智能监测终端和监控中心服务器两部分组成.智能终端完成发电设备的智能在线监测与实时分析;监控中心服务器实现发电设备的远程监测与诊断分析.系统的设计开发为新能源发电设备安全运行提供重要保障,同时也为新能源机组振动特性研究提供了有效工具.     

无人值守变电站电力设备音频监测及故障诊断系统

针对智能变电站的无人值守需求及现有故障诊断系统的不足,提出一种电力设备音频监测及故障诊断系统。根据变电站电力设备音频信号信噪比较低的特点,采用具有强鲁棒性的梅尔频率倒谱系数作为判断音频信号异常的特征参数,在此基础上根据音频特征构成多样本观测序列,并采用隐马尔科夫模型进行故障诊断,通过对比对数似然估计概率的输出值确定故障类型。该方法具有实时性较强的优势,也避免了现有故障诊断方法要求较大样本容量的缺陷。实验结果表明,该故障诊断系统具有较高的识别率和鲁棒性。     

Online knowledge validation with prudence analysis in a document management application

Prudence analysis (PA) is a relatively new, practical and highly innovative approach to solving the problem of brittleness in knowledge based system (KBS) development. PA is essentially an online validation approach where as each situation or case is presented to the KBS for inferencing the result is simultaneously validated. Therefore, instead of the system simply providing a conclusion, it also provides a warning when the validation fails. Previous studies have shown that a modification to multiple classification ripple-down rules (MCRDR) referred to as rated MCRDR (RM) has been able to achieve strong and flexible results in simulated domains with artificial data sets. This paper presents a study into the effectiveness of RM in an eHealth document monitoring and classification domain using human expertise. Additionally, this paper also investigates what affect PA has when the KBS developer relied entirely on the warnings for maintenance. Results indicate that the system is surprisingly robust even when warning accuracy is allowed to drop quite low. This study of a previously little touched area provides a strong indication of the potential for future knowledge based system development.     

基于Multi-agent的实时系统运行故障监控研究

软件密集型装备中常常包含着许多担负监测和控制作用的嵌入式实时系统,它们常常属于安全关键或者任务关键系统(safety-critical/mission-critical system)。为了能够有效解决该类系统中的软件故障检测、诊断与修复任务,本文提出了基于Multi-agent的实时系统运行故障监控框架,旨在利用在多agent的协作构建运行故障监控系统来在系统运行当中验证系统是否满足时序逻辑描述的性质规约,并采用具体的算法进行故障定位和修复。     

电力GIS中多层次智能故障诊断技术的研究

GIS因其强大的数据和空间分析功能，已被广泛应用于电力系统的各个方面。随着配网规模不断扩大，其运行方式变得更复杂，采用传统的、单一的故障诊断方法已不能满足实际系统的需要。本文提出一种分布式多层次智能故障诊断方法，它依据配电设备的类型进行故障诊断任务的分解，对分解后的子系统在多层智能推理策略的控制下按系统结构层次进行协同故障诊断，将最终的诊断结果在AM／FM／GIS中实时、动态地显示出来。该方法在大型复杂的配电系统中应用效果特别显著。     

基于霍尔电流传感器的电源监控系统设计

针对交直流电源线路中电流的实时监控问题,设计了一种基于霍尔电流传感器的电源监控系统。在该系统中,电流传感器CSNE151—100基于霍尔磁补偿原理,将被测电流转换为具有线性关系的电压信号。系统中的数字信号处理器（ DSP）采集传感器的测量值并通过控制开关实现对电源的过流保护。在实验室对系统进行了测试,实验结果表明：该电源监控系统具有响应速度快、测量精度高的优点。该系统已应用到工程实践中,工作稳定可靠,具有很好的实用价值。     

基于强跟踪滤波器的开关变换器故障诊断

Buck变换器又称降压变换器、串联开关稳压电源、三端开关型降压稳压器。广泛用于计算机、家用电器、控制系统和通信系统中的电源几乎都是开关电源,具有超快速负载动态响应、高功率变换效率、高功率密度、低电压、大电流的特性.开关变换器作为它的主要电路,故障率较高,对它故障诊断研究是有意义的。基于强跟踪滤波理论,给出了一种开关变换器电路故障实时诊断的一种方法。该方法以建立一种开关变换器电路状态空间模型为基础,利用强跟踪滤波器对电路状态及元件参数进行估计,当元件参数发生软、硬型故障时,根据强跟踪滤波器对元件参数的跟踪结果及修正的Bayes分类算法,可实时诊断开关变换器电路中的元件故障。仿真结果证明了该方法的有效性。     

10kV配网电容器在线监测与故障诊断系统

目前大多数电力电容器状态监测系统存在实时性不足和数据频密度不足的缺点,难以实现准确的在线故障预判,易导致故障处理滞后或误报等不良后果。文章立足于电力电容器运行和维护的实际需求,构建了一套完整的电力电容器故障在线监测系统,并给出了一套采用神经网络融合电容器电流、电容、电阻和电压等信息的故障诊断模型和方法,并在实际中进行了应用。在实际应用中,该系统能及时并准确地对电容器的异常状态和故障特征进行捕捉,避免了故障判断的滞后性,提高了获得数据的准确性,能够提高电网设备的运行和维护效率,提升电网运行可靠性。     

基于大数据的风电设备远程故障监测与诊断系统研究

大数据、云计算等新一代信息技术的快速发展,推动风电设备状态监测技术进步。文章通过风电设备远程监测与故障诊断平台建设实践,从系统功能、平台建设、故障分析等层面,论述了如何基于大数据实现对风机故障预诊断,提升风力发电机组监控与管理水平。通过对机组运行状态的实时在线监测,利用系统内智能数据报警策略准确筛选机组异常,借助于人工智能分析诊断系统和远程专家的综合分析评价,滚动预测故障未来的发展趋势,帮助用户优化风机维护检修工作,减少停机损失,降低维护成本,提高风机利用率。在集控中心预检测平台建立大数据存储、处理、分析、诊断服务器,收集风场各项数据后与专业厂家存储的数据资源整合,建立各种类型风机故障模型,集控中心培训诊断分析师可通过对比找出故障曲线特点和规律,对实时数据经诊断分析后提出整改措施,提前预防风机故障的扩大,开发相关监视及报警系统,联合风机传统监控系统提高监控中心的设备管理能力。     

On-line robust identification of tool-wear via multi-sensor neural-network fusion

Real-time identification and monitoring of tool-wear in shop-floor environments is essential for the optimization of machining processes and the implementation of automated manufacturing systems. This paper analyzes the signals from an acoustic emission sensor and a power sensor during machining processes, and extracts a set of feature parameters that characterize the tool-wear conditions. In order to realize real-time and robust tool-wear monitoring for different cutting conditions, a sensor-integration strategy that combines the information obtained from multiple sensors (acoustic emission sensor and power sensor) with machining parameters is proposed. A neural network based on an improved backpropagation algorithm is developed, and a prototype scheme for the real-time identification of tool-wear is implemented. Experiments under different conditions have proved that a higher rate of tool-wear identification can be achieved by using the sensor integration model with a neural network. The results also indicate that neural networks provide a very effective method of implementing sensor integration for the on-line monitoring of tool abnormalities.     

Fault-tolerant cooperative output regulation for multi-vehicle systems with sensor faults

This paper presents a unified framework of fault diagnosis and fault-tolerant cooperative output regulation (FTCOR) for a linear discrete-time multi-vehicle system with sensor faults. The FTCOR control law is designed through three steps. A cooperative output regulation (COR) controller is designed based on the internal mode principle when there are no sensor faults. A sufficient condition on the existence of the COR controller is given based on the discrete-time algebraic Riccati equation (DARE). Then, a decentralised fault diagnosis scheme is designed to cope with sensor faults occurring in followers. A residual generator is developed to detect sensor faults of each follower, and a bank of fault-matching estimators are proposed to isolate and estimate sensor faults of each follower. Unlike the current distributed fault diagnosis for multi-vehicle systems, the presented decentralised fault diagnosis scheme in each vehicle reduces the communication and computation load by only using the information of the vehicle. By combing the sensor fault estimation and the COR control law, an FTCOR controller is proposed. Finally, the simulation results demonstrate the effectiveness of the FTCOR controller.