磁浮列车悬浮控制系统电磁铁故障诊断技术研究

针对磁浮列车的电磁铁的部分失效故障会导致悬浮控制系统的结构参数发生变化,影响该支撑点的稳定悬浮问题,研究了悬浮控制系统电磁铁故障诊断及其容错控制问题,提出一种将执行器故障导致的控制系统参数变化等效为控制输入变化的算法,实现了对执行器故障的量化指示,给出了执行器(电磁铁)的故障百分比.通过仿真和实验验证了算法的有效性.     

Integration of fault diagnosis and control based on a trade-off between fault detectability and closed loop performance

This paper presents a novel methodology for simultaneous optimal tuning of a fault detection and diagnosis (FDD) algorithm and a feedback controller for a chemical plant in the presence of stochastic parametric faults. The key idea is to propagate the effect of time invariant stochastic uncertainties onto the measured variables by using a Generalized Polynomial Chaos (gPC) expansion and the nonlinear first principles’ model of the process. A bi-level optimization is proposed for achieving a trade-off between the fault detectability and the closed loop process variability. The goal of the outer level optimization is to seek a trade-off between the efficiency of detecting a fault and the closed loop performance, while the inner level optimization is designed to optimally calibrate the FDD algorithm. The proposed method is illustrated by a continuous stirred tank reactor (CSTR) system with a fault consisting of stochastic and intermittent variations in the inlet concentration. Beyond achieving improved trade-offs between fault detectability and control, it is shown that the computational cost of the gPC model based method is lower than the Monte Carlo type sampling based approaches, thus demonstrating the potential of the gPC method for dealing with large problems and real-time applications.     

含控制时滞系统的实时故障诊断和最优容错控制

研究含有控制时滞的线性系统的故障诊断方法和最优容错控制问题.给出了最优容错控制律的存在唯一性条件,提出了最优容错控制律的设计算法.通过构造增广的降维状态观测器,设计了在线诊断故障的故障诊断器并同时实现了系统状态的观测,解决了最优容错控制的物理不可实现问题.利用观测器的输出得到物理可实现的动态最优容错控制律.仿真实例验证了故障诊断方法和动态最优容错控制律的有效性.     

Conceptual design of remote monitoring and fault diagnosis systems

Condition monitoring and fault diagnosis are of fundamental importance for many industrial systems. In the last decade, substantial research efforts have been made on the surveillance and diagnosis systems for different types of equipment, with the approach of integrating information technologies and intelligent computing methods. This paper presents the conceptual design of a distributed information system of condition monitoring and fault diagnosis for a growing number of gas turbine-based power generation systems. Each individual information system that monitors a specific gas turbine system, locally deployed in a power plant, is linked to another information system, deployed at the manufacturer's site, which oversees all the gas turbine systems in parallel. The systems are constructed on the basis of COM components, which are conceptually separated into three tiers. Subsequently, this paper proceeds to present a generic business domain model with components encapsulating physical entities of interest. Finally, this paper addresses the interactions among components, which considerably affect the performance of the system including efficiency and effectiveness. It has been identified that both asynchronous and synchronous communication mechanisms are required for exchanging information for different scenarios. COM+ services are also required for supporting object pooling, transaction coordination, and security control.     

Probabilistic design of optimal sequential decision-making algorithms in learning and control

This survey is focused on certain sequential decision-making problems that involve optimizing over probability functions. We discuss the relevance of these problems for learning and control. The survey is organized around a framework that combines a problem formulation and a set of resolution methods. The formulation consists of an infinite-dimensional optimization problem. The methods come from approaches to search optimal solutions in the space of probability functions. Through the lenses of this overarching framework we revisit popular learning and control algorithms, showing that these naturally arise from suitable variations on the formulation mixed with different resolution methods. A running example, for which we make the code available, complements the survey. Finally, a number of challenges arising from the survey are also outlined.     

Model-based diagnosis and fault tolerant control for ensuring torque functional safety of pedal-by-wire systems

This paper presents a model based approach for defining automotive functional safety requirements and provides a solution to ensure functional safety through model-based diagnosis and fault tolerant control. This model-based approach is consistent with ISO 26262 – functional safety standard. In particular, this paper presents the necessary steps for defining and implementing functional safety requirements, including item and function definition, Hazard Analysis and Risk Assessment, as well as the design of a model-based diagnostic and fault tolerant control (FTC) system that can lead to a systematic solution to automotive functional safety problems. The methodology proposed in this paper is applied to the problem of torque functional safety of pedal-by-wire systems.     

An application of expert systems techniques to the on-line control and fault diagnosis of a mixing process

An application of expert hierarchical control is described in this paper. The control is implemented in a two-level configuration, where the lower layer performs direct regulation control and the upper layer performs supervisory functions. In the regulation layer, a rule-based controller performs the regulation task, where the controller is constructed upon causal relations between subsystems. The control action is inferred from the measurement of both controlled and noncontrolled variables. In the supervisory layer, the main function is a fault diagnosis system which diagnoses faults on-line. The diagnosis is based upon reasoning from the structure of the system and the functions of its components, and efficient diagnosis is achieved by dividing the system into several subsystems. The overall technique has been successfully implemented on a pilot scale mixing process under on-line computer control.     

Closed-loop fault diagnosis based on a nonlinear process model and automatic fuzzy rule generation

In this contribution a new approach for fault detection and diagnosis (FDD) for nonlinear processes is presented. A nonlinear fuzzy model with transparent inner structure is used for the generation of relevant symptoms. The resulting symptom patterns are classified with a new self-learning classification structure based on fuzzy rules. The approach is successfully applied to an electro-pneumatic valve in a closed control loop.     

Slow feature analysis for monitoring and diagnosis of control performance

Recently, slow feature analysis (SFA), a novel dimensionality reduction technique, has been adopted for integrated monitoring of operating condition and process dynamics. By isolating temporal behaviors from steady-state information, the SFA-based monitoring scheme enables improved discrimination of nominal operating point changes from real faults. In this study, we demonstrate that the temporal dynamics is an additional indicator of control performance changes, and further exploit its unique efficacy in control performance monitoring. Because of its data-driven nature and ease from first-principle knowledge, the SFA-based monitoring scheme allows an overall assessment of the plant-wide control performance and is compatible with different control strategies. An attractive feature of the SFA-based approach compared to existing ones is that generic process monitoring indices are used, which renders contribution plots naturally applicable to real-time diagnosis of control performance. As a result, potential fault variables as root causes of control performance changes can be identified, including not only controlled variables (CV) but also manipulated variables (MV) and disturbance variables (DV). Simulated and experimental studies demonstrate the effectiveness of the proposed method.     

Evaluating the change of software fault behavior with dataset attributes based on categorical correlation

Utilization of data mining in software engineering has been the subject of several research papers. Majority of subjects of those paper were in making use of historical data for decision making activities such as cost estimation and product or project attributes prediction and estimation. The ability to predict software fault modules and the ability to correlate relations between faulty modules and product attributes using statistics is the subject of this paper. Correlations and relations between the attributes and the categorical variable or the class are studied through generating a pool of records from each dataset and then select two samples every time from the dataset and compare them. The correlation between the two selected records is studied in terms of changing from faulty to non-faulty or the opposite for the module defect attribute and the value change between the two records in each evaluated attribute (e.g. equal, larger or smaller). The goal was to study if there are certain attributes that are consistently affecting changing the state of the module from faulty to none, or the opposite. Results indicated that such technique can be very useful in studying the correlations between each attribute and the defect status attribute. Another prediction algorithm is developed based on statistics of the module and the overall dataset. The algorithm gave each attribute true class and faulty class predictions. We found that dividing prediction capability for each attribute into those two (i.e. correct and faulty module prediction) facilitate understanding the impact of attribute values on the class and hence improve the overall prediction relative to previous studies and data mining algorithms. Results were evaluated and compared with other algorithms and previous studies. ROC metrics were used to evaluate the performance of the developed metrics. Results from those metrics showed that accuracy or prediction performance calculated traditionally using accurately predicted records divided by the total number of records in the dataset does not necessarily give the best indicator of a good metric or algorithm predictability. Those predictions may give wrong implication if other metrics are not considered with them. The ROC metrics were able to show some other important aspects of performance or accuracy.     

基于粗糙集理论的煤磨故障诊断模型

针对水泥煤磨系统的常见故障,将基于粗糙集理论的数据挖掘技术应用于水泥煤磨故障诊断。结合具体的数据,采用粗糙集的方式,建立了故障诊断系统,通过该系统对识别数据进行故障诊断实验,表明该系统能有效地对煤磨堵仓和钢球小故障进行诊断。诊断结果的精度可以满足现场应用的要求。     

水轮机调速器的智能故障诊断与控制研究

针对水轮机调速器故障诊断和容错控制难题,提出了一套适合于非线性时变系统的智能容错控制策略。该策略采用多传感器数据融合算法和符号法定性故障诊断法,以实现测频故障和液压随动系统故障检测;并探讨了控制算法重构规律,实现传感器故障时的无扰切换和稳定运行。该策略已在单机15MW高水头冲击水轮发电机上应用,效果良好。     

LUBRES: An expert system development and implementation for real-time fault diagnosis of a lubricating oil refining process

An expert system LUBRES (LUBricating oil Refining Expert System) is introduced in this paper. It helps plant operators in monitoring and diagnosing of abnormal situations in refining process of lubricating oil. The LUBRES structure, the knowledge base, and the inference machine are presented in detail. A new strategy is proposed for conflicts resolution – the sorting strategy of antecedents, and a selection strategy of knowledge rules in the memory knowledge base. Knowledge acquisition mechanism is based on an empirical knowledge table, while knowledge verification is carried out based on the directed graph approach. C++Builder and SQL Server 2000 have been used in developing the proposed system. LUBRES has been successfully implemented in Microsoft Windows Server environment. For 1 year, LUBRES has been used for monitoring and diagnosing of refining process of the lubricating oil. The industrial application of LUBRES proved its high reliability and accuracy.     

Robust fault diagnosis and fault‐tolerant control for non‐Gaussian uncertain stochastic distribution control systems

The purpose of fault diagnosis of stochastic distribution control systems is to use the measured input and the system output probability density function to obtain the fault estimation information. A fault diagnosis and sliding mode fault‐tolerant control algorithms are proposed for non‐Gaussian uncertain stochastic distribution control systems with probability density function approximation error. The unknown input caused by model uncertainty can be considered as an exogenous disturbance, and the augmented observation error dynamic system is constructed using the thought of unknown input observer. Stability analysis is performed for the observation error dynamic system, and the H_∞ performance is guaranteed. Based on the information of fault estimation and the desired output probability density function, the sliding mode fault‐tolerant controller is designed to make the post‐fault output probability density function still track the desired distribution. This method avoids the difficulties of design of fault diagnosis observer caused by the uncertain input, and fault diagnosis and fault‐tolerant control are integrated. Two different illustrated examples are given to demonstrate the effectiveness of the proposed algorithm. Copyright © 2016 John Wiley & Sons, Ltd.     

基于简化因果图的工业过程故障根本变量诊断

在对工业过程故障进行根本原因诊断时,由于过程的自身特性和反馈控制等因素的干扰,使得变量因果图过于复杂从而使故障传播路径难以解释且不能找到导致故障的根本变量。提出一种简化因果图的方法,通过两步走对收敛交叉映射法构建的因果图实现简化,保留主要的故障传播路径。首先采用模糊综合评判法判别因果图中不确定性的关系;然后通过求解最大生成树,得到赋权无向图,并根据变量间因果关系选取根节点,分析赋权无向图获得新路径,从而将其改进成赋权有向图。在田纳西—伊斯曼过程进行验证实验,并与传统收敛交叉映射法进行比较,结果表明所提出方法的有效性。     

基于重构技术工业故障诊断OPC数据平台的设计

当今日益飞速发展的自动化控制技术使得工厂的自动化程度越来越高,然而带来的问题就是一旦检测设备发生故障,会带来控制系统的崩溃,轻则带来经济损失重则造成工业事故.本文就工业故障诊断技术利用OPC(OLE for Processing Control)技术设计1种容错控制数据平台来解决这一问题.通过OPC服务获得工业现场数据,利用故障诊断技术对工业数据进行诊断,检测辨识故障传感器,隔离故障传感器,采用已知数据回归法(Known Data Regression)重构故障数据,作为上层控制软件的输入从而可以使控制系统稳定运行,故障监控软件给操作人员发出检修信号.实验室仿真结果表明该平台可以稳定运行,这为故障诊断和容错技术实际工程应用奠定了基础.     

一种水下机器人传感器故障诊断与容错控制方法

采用自适应滤波器ＦＩＲ 对水下机器人进行在线自适应建模,并利用ＬＭＳ算法来调节滤波器的权系数．通过对滤波器权系数和误差信号平方的分析,实时检测出传感器的故障,并应用ＦＩＲ 滤波器输出替代故障传感器信号,实现传感器故障情形下水下机器人容错控制．应用该方法对Ｏｕｔｌａｎｄ１０００水下机器人传感器的故障进行检测和容错,实验结果表明所提故障检测方法准确可靠,具有较好的容错效果．     

基于模糊控制和大数据算法模型的电力运维故障诊断设备方法

针对现有技术中对电力运维故障检测灵敏度低、诊断误差大等问题,设计了一种新型故障诊断方案。该方案将PID模糊控制计算器与大数据算法模型相结合,并采用实时布线的方法减少诊断面积,基于改进型大数据算法模型提取电力运维设备故障数据特征,对电力运维设备运行工况构建诊断网络,通过分析电力运维设备工况的功能系统完成数据诊断。为了减少诊断误差,该研究设计了一种故障诊断设备,采用集成芯片化设计和算法程序,减小体积的同时保证检测结果的准确性。实验结果表明,该研究方法故障诊断误差小,准确率最高达到98.6%。     

Fault diagnosis of power electronic system based on fault gradation and neural network group

We propose a new fault diagnosis approach with fault gradation using BP (back-propagation) neural network group consisting of 3 sub BP neural networks. According to the hazard extents and the occurrence frequencies of different faults, the faults are divided into different grades. The higher the fault grade, the larger the number of the used sub neural networks is. Experimental results show that our approach makes the correctness rate of the fault diagnosis rise greatly (from less than 95.0% to 99.5%) and the performance of the whole fault diagnosis system gets much better especially for the on-line complex systems. The approach proposed in this paper also can be extended to other complex fault diagnosis systems, such as mechanical systems.