Research of Multi-Agent System based satellite fault diagnosis technology

Following the theory of Multi Agent System (MAS) and using series wound structure and shunt wound structure of Agents, the performance of Agent was improved to satisfy the need of satellite fault diagnosis, and a tridimensional MAS model of satellite fault diagnosis was thus established for the MAS based planar diagnosis system, which decentralizes the whole diagnosing task into subtasks to be performed by different functional Agents to make the complicated fault diagnosis very simple and the diagnosis system more intelligent. This method improved the reliability and accuracy of diagnosis and made the maintenance and upgrading of the satellite fault diagnosis system very easy as well.

A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment.Classical methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related symptoms.As an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD conditions.However,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with HD.In this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)model.The MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD identication.The CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD features.We evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation criteria.The AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on average.Therefore,the AHDD system proposed herein has the highest capability to identify patients with HD.This system can be used by medical practitioners to diagnose HD efciently。

Distributed fault diagnosis observer for multi-agent system against actuator and sensor faults

Component failures can cause multi-agent system(MAS)performance degradation and even disasters,which provokes the demand of the fault diagnosis method.A distributed sliding mode observer-based fault diagnosis method for MAS is developed in presence of actuator and sensor faults.Firstly,the actuator and sensor faults are extended to the system state,and the system is transformed into a descriptor system form.Then,a sliding mode-based distributed unknown input observer is proposed to estimate the extended state.Furthermore,adaptive laws are introduced to adjust the observer parameters.Finally,the effectiveness of the proposed method is demonstrated with numerical simulations.

Research on reconfigurable collaborative remote diagnosis system

The function-layer model and working model of collaborative remote fault diagnosis system （FDS）, which includes three layers： task layer, collaboration layer and diagnosing layer, are proposed. The running mechanism of the system is discussed. A collaborative FDS may consist of several subsystems running at different places and the subsystem consists of several fimction modules. A structure centered on data-bus is adopted in subsystem. All the function modules in subsystem are encapsulated into software intelligent chips （SICs） and SIC can but connect with data-bus. So, it is feasible to reuse these diagnosis fimction modules and the structure of subsystem in different diagnosis applications. With the reconfigurable SICs, several different function modules can reconstruct quickly some different diagnosis subsystems in different combinations, and some subsystems can also reconfigure a specified collaborative FDS.

Multi-agent System Optimized Reconfiguration of Shipboard Power System

Reconfigurability of the electrical network in a shipboard power system （SPS） after its failure is central to the restoration of power supply and improves survivability of an SPS. The navigational process creates a sequence of different operating conditions. The priority of some loads differs in changing operating conditions. After analyzing characteristics of typical SPS, a model was developed used a grade III switchboard and an environmental prioritizing agent （EPA） algorithm. This algorithm was chosen as it is logically and physically decentralized as well as multi-agent oriented. The EPA algorithm was used to decide on the dynamic load priority, then it selected the means to best meet the maximum power supply load. The simulation results showed that higher priority loads were the first to be restored. The system satisfied all necessary constraints, demonstrating the effectiveness and validity of the proposed method.

Multi-agent and ant colony optimization for ship integrated power system network reconfiguration

Electric power is widely used as the main energy source of ship integrated power system(SIPS), which contains power network and electric power network. SIPS network reconfiguration is a non-linear large-scale problem. The reconfiguration solution influences the safety and stable operation of the power system. According to the operational characteristics of SIPS, a simplified model of power network and a mathematical model for network reconfiguration are established. Based on these models, a multi-agent and ant colony optimization(MAACO) is proposed to solve the problem of network reconfiguration. The simulations are carried out to demonstrate that the optimization method can reconstruct the integrated power system network accurately and efficiently.

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

配电网电源系统中的故障诊断与智能恢复策略研究

随着电力系统的不断发展,配电网电源系统作为电力供应链的重要组成部分,其稳定运行对于保障电力系统的可靠性具有重要意义。文章旨在研究配电网电源系统中的故障诊断与智能恢复策略,为配电网电源系统的安全、高效运行提供坚实的技术支撑。通过系统剖析配电网电源系统的组成结构、常见故障类型及其原因、先进的故障诊断技术等内容,为提高系统运行的稳健性和智能化水平提供技术支持。同时,重点关注智能恢复策略的协调控制、故障隔离和系统重构等核心内容,并通过案例分析评估了这些策略在实际应用中的效果。

航天器控制系统的自主诊断重构技术

自主诊断重构是确保航天器控制系统安全可靠自主运行的关键技术之一.本文针对航天器资源严重受限的客观情况,改变传统一味追求华丽算法与豪华配置的设计理念,从航天器的自身特性出发,深入挖掘诊断重构问题的本质,提出基于可诊断性与可重构性评价设计的研究思路.文章首先介绍了航天器自主诊断重构技术的发展现状,分析了当前技术的不足之处并探究了其深层原因;然后,提出了通过可诊断性与可重构性评价设计从根本上提高系统自主诊断重构能力的全新思路,梳理了其研究内容与研究概况;最后,对航天器控制系统自主诊断重构技术的发展趋势进行了展望.

Petri网在电力系统中的应用

介绍了Petri网的基本原理及其在电力系统中的应用。通过收集和整理国内外相关学术期刊和会议论文集中已发表的数十篇有关Petri网在电力系统中应用研究方面的学术论文,按故障诊断、系统恢复、配电系统重构、机组组合、网络拓扑分析、可靠性分析、继电保护建模与混杂电力系统分析等方面分别进行了分类述评。从现有的研究工作情况来看,Petri网在电力系统中的应用涉及面很广,并受到了比较普遍的重视。

探索企业的设计与诊断

介绍了企业设计的任务与原理、概念设计的结构、必须转变的观念、企业诊断的战略及革新组织与管理。值得注意的是 ,根据系统工程与分析、可重组性以及增强先进技术与系统集成管理的在职教育 ,可以成功地实施企业设计与诊断。

基于细胞免疫应答理论重建故障诊断智能体

借鉴生物免疫中的独特性网络调节理论,将进化的单一诊断Agent对应为免疫系统中的抗体,诊断系统中多Agent群体适应度增加量作为抗原,并依据生物系统的相似性理论,构造了诊断多智能体的细胞免疫型智能体重构控制的结构模型;提出了一种基于行为Petri网技术的工作流模型重建故障诊断智能体的算法,实现故障诊断系统中的多智能体的群体进化、自组织与突现,并给出Agent重构的非线性动力学解释。

智能变电站智能电子设备在线评估及动态重构

设备在线健康诊断及失效功能恢复是智能变电站可靠运行的保障。阐述了智能变电站智能电子设备(IED)在线评估与动态重构技术发展的迫切性。基于现有的IEC 61850标准和即插即用技术,提出了在线评估与动态重构冗余备用的技术方案,探讨了即插即用、诊断模型、IED数据库自动生成、在线评估和动态重构等关键内容,分析了当前亟须解决的主要问题,符合新一代智能变电站发展方向。

航空发动机传感器故障诊断与自适应重构控制

针对航空发动机控制系统中最易发生故障的传感器,提出了基于状态观测器的故障诊断与自适应重构控制方法。设计了多个状态观测器用于传感器的状态估计,采用阈值判别法对传感器进行实时故障监视与诊断,并用正常传感器测量值的加权均值代替故障传感器测量值反馈回闭环系统,实现了对发动机闭环系统的自适应重构控制。数字仿真结果表明,该方法能及时准确地定位故障,并进行有效的自适应重构控制,将其应用于航空发动机的故障诊断与自适应重构控制是可行、有效的。

无人机容错飞行控制系统研究与应用

文章针对某型无人机高可靠性要求,提出一种容错飞行控制系统构架,同时研究飞行控制系统的故障诊断和容错控制;首先对飞行控制系统三大组成部分传感器子系统、机载计算机、伺服子系统进行余度设计;然后对三部分进行故障诊断并根据故障诊断信息进行自适应容错控制;仿真验证与应用结果表明,该方案实现了部分自主维护和自适应故障隔离与重构功能,有效地提高了系统的故障检测率,保证了系统的可靠性与容错能力,同时也为系统调试及维护提供了极大的方便。

汽车底盘集成系统的重构控制技术评述

汽车底盘集成控制是当前底盘控制技术的发展方向,主要包括底盘集成控制对象、底盘集成控制策略、底盘集成控制网络和重构控制技术,重构控制将失效的子系统的控制效果分配给其他健全的子系统,以补偿失效的子系统的影响,保证汽车行驶的性能。介绍了底盘集成系统重构控制的结构,分析了重构控制的故障诊断方法和重构控制方法,以及研究的关键技术和难点问题。底盘集成系统的故障诊断及其重构控制对提高底盘集成系统的自适应能力和智能化水平具有重要意义。

可重组制造系统及其关键技术

分析了多品种、变批量的生产方式对制造系统的要求,对适应这一发展趋势的可重组制造系统(reconfig-urable manufacturing system,RMS)的特性和组成进行了描述,提出了可重组制造系统的结构和设计原则,将可重组制造系统的关键技术归纳为布局规划与优化技术、系统建模技术、可重组机床设计技术、模块化控制器技术、可拼接物流技术、构件集成和整合技术,以及系统快速可诊断技术并分别予以阐述.

制造系统分布式柔性可重组状态监测与诊断技术研究

随着制造系统的分散化、柔性化与快速重组技术发展,研究与之相适应的具有分步式柔性可重组特性的状态监测与诊断技术具有重要意义。研究如何运用“柔性可重组”思想构建分布式柔性可重组状态监测与诊断系统,建立了系统的硬件及软件模型,重点分析了模型中各部分的功能与结构。

基于改进LS-SVM的航空发动机传感器故障诊断与自适应重构控制

提出了一种基于改进LS-SVM的航空发动机传感器故障诊断与自适应重构控制方法.该方法通过给误差变量赋予不同权值因子提高LS-SVM的鲁棒性,采用修剪算法提高LS-SVM的稀疏性;该方法从某涡扇发动机输入输出空间中建立其正常模型,采用阈值判别法对传感器故障进行实时监视与诊断,并用模型输出值代替故障传感器测量值反馈回闭环控制系统,实现对发动机的自适应重构控制.仿真结果表明,该方法能及时准确地定位故障,并进行有效的自适应重构控制.

基于灰色动态预测的风力发电系统容错控制策略研究

提出了一种新型的风力发电系统的主动容错控制策略。引入灰色动态预测模型,对传感器故障进行预测。基于风力发电系统模糊模型,设计容错控制器,利用邻近两状态反馈回路补偿失效回路的系统重构方法,重新确定系统反馈控制律。仿真结果表明在额定风速以下,传感器发生故障时系统的功率系数和叶尖速比均能保持在最优值,从而实现额定风速以下的最大风能捕获。