Robust diagnosis with high protection to gas turbine failures identification based on a fuzzy neuro inference monitoring approach

Modern industry requires the development of new monitoring and diagnostic procedures, which enable the detection, localization, and isolation of faults. For sustainable solutions in terms of operational safety and availability, while bringing out zero accidents, zero downtime, and zero faults, for a trend acting on environmental issues. Towards this development, this work proposes solutions for the monitoring of gas turbines and their real-time implementation, in order to approximate and predict the degradation of the components of this system, by an approach of faults detection and isolation, based on an adaptive neural-fuzzy inference system. This will develop a reliable approach to maintain and monitor gas turbines, in case of failure or accident to prevent in real-time and makes it possible to achieve high power with efficiency and small footprint with High performance by operating this rotating machine. However, the application of the Adaptive Neuro-Fuzzy Inference System Observer-Based Approach, makes it possible to increase the life of the examined turbine and keep better reliability for their monitoring system and satisfy the techno-economic and environmental performance impacts. For the purpose of controlling failures and the occurrence of turbine system malfunctions, and avoiding their consequences on the safety and productivity of the installation.     

Interest point based face recognition using adaptive neuro fuzzy inference system

Multimedia Tools and Applications - In this paper, an efficient face recognition method using AGA and ANFIS-ABC has been proposed. At first stage, the face images gathered from the database are...     

Virtual sensors design for active fault tolerant control system applied to a winding machine

This paper presents a method for designing a virtual sensor in the case of Fault Tolerant Control. The whole approach, described in the paper, is applied to a winding machine. The process is modelled by a Linear Time Varying (LTV) model. The control of the web tension is based on the estimated or virtual measurements as feedback variables. The main contribution concerns the design of an accurate bank of observers able to both isolate and estimate sensor faults while preserving the system’s integrity in terms of structure and performances. The objective of Active Fault Tolerant Control System, is to maintain current performances close to desirable ones and preserve stability conditions when faults occur. The presented method allows one to isolate and to estimate sensor faults. The effectiveness and performances of the strategy are illustrated on the winding machine in the case of a complete loss of sensor.     

基于模糊推理水下作业系统运动控制研究

水下作业系统是运动学冗余系统.本文将模糊推理方法融入基于任务优先运动学控制算法,对系统载体与机械手进行协调运动分配,同时对系统多个任务进行优化.通过带有3自由度水下机械手的水下作业系统进行算例仿真研究,说明运动控制算法的有效性,     

A fuzzy inference system for fault detection and isolation: Application to a fluid system

This work focuses on the design and implementation of a fuzzy inference system for fault detection and isolation (FDI) which can learn from example fault data, and the determination of a suitable optimisation strategy for the membership functions. A FDI system was developed which is based on adaptive fuzzy rules. A number of optimisation strategies were then applied; it was found that an evolutionary algorithm not only produced the best results but did so with relatively little processing effort and with excellent consistency.The adaptive fuzzy system, thus optimised, was tested against a neural network, which was trained to produce analogue outputs as an indication of fault magnitude. The fuzzy solution produced the best accuracy.We can conclude that an adaptive fuzzy inference system for FDI, using an evolutionary algorithm to learn from examples, can provide an accurate and readily comprehensible solution to diagnosing and evaluating fluid process plant faults.     

基于模糊控制的微型燃机燃料压力控制

针对微型燃气轮机在控制时由于压力不恒定而导致的运行效率低的问题,将模糊PID控制思想运用到控制系统。在传统的PID控制下加入模糊控制,使其PID参数具有在线自整定功能。仿真结果表明,该方法能够很好地提高压力控制效果。     

基于模糊推理的汽车辅助驾驶系统控制算法

在建立汽车辅助驾驶系统模型的基础上,指出满足驾驶员的驾驶特征是车辆控制的一个重要指标,此外针对驾驶员驾驶行为的不精确性,提出了以模糊推理为基础的上位控制方法,并对其进行了现场实验。实验结果表明,用模糊控制理论模拟驾驶行为的不精确性是可行的。通过模糊控制自车的速度,能够实现自车在多种工况下保持安全状态。     

Active fault tolerant control for a class of linear time-delay systems in finite frequency domain

This article deals with the problem of active fault tolerant control (AFTC) for a class of linear time-delay systems in a finite frequency domain. A new ?_∞ controller in generalised internal model control architecture with an observer-based fault estimator is proposed for the AFTC system. Based on online fault estimation and accommodation, the closed-loop system stability and ?_∞ performance for both fault free and faulty cases are guaranteed. With the aid of the generalised Kalman–Yakubovich–Popov lemma for time-delay systems, sufficient conditions on the existence of such an AFTC system ensuring simultaneous finite frequency estimation and control performance are derived. The calculation procedure of the parameter matrices of the fault estimator and ?_∞ controller is also proposed. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.     

基于模糊Petri网的无线顺序控制系统的故障诊断

通过分析基于无线传感器网络的顺序控制系统,给出它的故障传播规则。针对模糊Petri网在故障诊断中的置信度模糊推理算法的不足,进行了添加阈值判断的改进。运用改进后的模糊Petri网推理算法对无线顺序控制系统进行故障诊断,计算控制器故障发生的概率,得出其中控制逻辑重新发送概率最大,理论结果与现场实际测试结果基本一致。     

基于增量型模糊控制的水轮机转速控制的研究

本文对模糊控制在水轮机调节的应用中可能出现的问题做了理论上的分析,运用仿真手段和提出了产量型模糊控制在水轮机调节应用中的运行模式,该运行模式具有较好的动静态特性和较强的鲁棒性,为模糊控制在水轮机调节中的应用打下理论上的基础。     

基于自适应神经网络模糊推理系统的蓄电池SOH预测

针对装甲车辆铅酸蓄电池健康状况影响因素复杂、难以准确预测的特点,提出了基于自适应神经网络模糊推理系统的蓄电池SOH预测模型。在确定模型的输入变量后,对其进行了MATLAB仿真和实测数据验证分析。结果表明,该模型具有很高的预测精度,在装甲车辆铅酸蓄电池SOH预测上具有很高的实用价值。     

Trusted consensus protocol for blockchain networks based on fuzzy inference system

The Journal of Supercomputing - Blockchain technology with its inherent security features revolutionizes the field of distributed networks and has become one of the significant areas of research....     

基于历史相遇概率的容迟容断网络主动拥塞控制算法

为了解决容迟容断网络(DTN)由于节点拥塞造成网络阻塞的问题,提出了一种基于历史相遇概率的主动拥塞控制算法。该算法提出了参考概率这一概念,可以通过节点的拥塞程度动态调整参考概率的大小,进而控制消息的转发条件,以达到对节点拥塞的避免与控制作用,并且在网络资源出现空闲时,可以提升空闲资源的利用率,提高整个网络的传输效率。仿真结果表明,该算法提高了整个网络的递交率,降低了负载比率及消息丢失率,在实现主动拥塞控制的同时也提升了网络的传输性能。     

基于多变量LS-SVM和模糊循环推理系统的负荷预测

智能电网环境下,电力需求响应的发展给传统用电模式带来重大变化,用户可以根据电能需求结合实时电价调整用电模式,这使得负荷预测变得更加复杂。通过相似日负荷序列局部形相似计算,选取样本数据,采用多输入双输出的最小二乘支持向量机(LS-SVM),对负荷和价格进行同时预测,得到初步预测结果。考虑需求响应条件下实时电价与负荷之间的相互影响,采用基于数据挖掘技术的模糊循环推理系统模拟人的思维过程,通过挖掘电价变化量、负荷变化量等变量之间的关联规则,模拟电价与负荷预测之间存在的博弈过程,对多变量最小二乘支持向量机预测算法的初步预测结果进行循环修改,直至负荷和电价预测结果趋于稳定。多变量最小二乘支持向量机不存在容易陷入局部最优等问题,并且有良好的泛化能力,基于改进的模糊关联规则挖掘算法和循环预测控制算法具有良好的完备性和鲁棒性,能够逼近现实环境的各种可能情况,修正负荷预测结果。针对某电网的实际预测结果表明,该方法具有较好的预测效果。     

A hybrid model based on adaptive-network-based fuzzy inference system to forecast Taiwan stock market

In recent years, many academy researchers have proposed several forecasting models based on technical analysis to predict models such as Engle, 1982, Cheng et al., 2010. After reviewing the literature, two major drawbacks are found in past models: (1) the forecasting models based on artificial intelligence algorithms (AI), such as neural networks (NN) and genetic algorithms (GAs), produce complex and unintelligible rules; and (2) statistic forecasting models, such as time series, require some basic assumptions for variables and build forecasting models based on mathematic equations, which are not easily understandable by stock investors. In order to refine these drawbacks of past models, this paper has proposed a model, based on adaptive-network-based fuzzy inference system which uses multi-technical indicators, to predict stock price trends. Three refined processes have proposed in the hybrid model for forecasting: (1) select essential technical indicators from popular indicators by a correlation matrix; (2) use the subtractive clustering method to partition technical indicator value into linguistic values based on an data discretization method; (3) employ a fuzzy inference system (FIS) to extract rules of linguistic terms from the dataset of the technical indicators, and optimize the FIS parameters based on an adaptive network to produce forecasts. A six-year period of the TAIEX is employed as experimental database to evaluate the proposed model with a performance indicator, root mean squared error (RMSE). The experimental results have shown that the proposed model is superior to two listing models (Chen’s and Yu’s models).     

一类时延网络控制系统的骱容错保成本控制

针对具有时延和参数不确定性的网络控制系统的研究。考虑系统的时延小于一个采样周期,传感器是时钟驱动,控制器和执行器是事件驱动。当执行器发生故障时,研究网络控制系统的Hoo容错保成本控制。依据所描述情况建立系统模型,基于Lyapunov稳定性理论、容错控制理论和线性矩阵不等式（LMIs）处理方法,推导出网络控制系统是渐近稳定的,并且得出系统的Hoo容错保成本控制的充分条件和系统的保成本上界。实例仿真证明结论的有效性。     

基于模糊控制MPPT的单相光伏并网发电系统

采用模糊控制算法实现光伏阵列最大功率点跟踪,利用软件锁相技术完成逆变器输出电流与电网电压的频率和相位同步。实验结果表明,该系统的设计能满足并网要求,而且模糊控制可有效消除最大功率点振荡现象。     

模糊神经网络抓斗纠偏系统故障诊断

针对抓斗纠偏系统复杂性、不确定性、模糊性的特点,提出基于故障树的模糊神经网络作为抓斗纠偏系统故障诊断的方法。该方法利用故障树知识提取抓斗纠偏系统故障诊断的输入变量和输出变量,引入模糊逻辑的概念,采用模糊隶属函数来描述故障的程度,利用Levenberg-Marquardt优化算法对神经网络进行训练,系统推理速度快、容错能力强,并通过实例分析验证了抓斗纠偏系统模糊神经网络故障诊断的有效性。     

Case study on distributed and fault tolerant system modeling based on timed automata

This article presents the modeling of a distributed fault-tolerant real-time application by timed automata. The application under consideration consists of several processors communicating via a Controller Area Network (CAN); each processor executes an application that consists of fault-tolerant tasks running on top of an operating system (e.g. OSEK/VDX compliant) and using inter-task synchronization primitives. For such a system, a model checking tool (e.g. UPPAAL) can be used to verify the complex time and logical properties formalized as safety or bounded liveness properties (e.g. end-to-end response time considering an occurrence of a fault). The proposed model reduces the size of the state-space by sharing clocks measuring the execution time of the tasks.     

Design of a fault tolerant control system incorporating reliability analysis and dynamic behaviour constraints

In highly automated aerospace and industrial systems where maintenance and repair cannot be carried out immediately, it is crucial to design control systems capable of ensuring desired performance when taking into account the occurrence of faults/failures on a plant/process; such a control technique is referred to as fault tolerant control (FTC). The control system processing such fault tolerance capability is referred to as a fault tolerant control system (FTCS). The objective of FTC is to maintain system stability and current performance of the system close to the desired performance in the presence of system component and/or instrument faults; in certain circumstances a reduced performance may be acceptable. Various control design methods have been developed in the literature with the target to modify or accommodate baseline controllers which were originally designed for systems operating under fault-free conditions. The main objective of this article is to develop a novel FTCS design method, which incorporates both reliability and dynamic performance of the faulty system in the design of a FTCS. Once a fault has been detected and isolated, the reconfiguration strategy proposed in this article will find possible structures of the faulty system that best preserve pre-specified performances based on on-line calculated system reliability and associated costs. The new reconfigured controller gains will also be synthesised and finally the optimal structure that has the ‘best’ control performance with the highest reliability will be chosen for control reconfiguration. The effectiveness of this work is illustrated by a heating system benchmark used in a European project entitled intelligent Fault Tolerant Control in Integrated Systems (IFATIS EU-IST-2001-32122).