基于随机有色网的柔性制造系统可用性评价方法

提出了一种柔性制造系统(FMS)的可用性评价新方法。针对传统可用性评价方法如故障树分析法、可靠图法等难以表达复杂系统的动态特性和系统内部的关联性的缺点,定义了一种基于随机Petri网和有色网对FMS生产过程进行表示的方法——SCPN(stochastic colored Petri nets)。通过构造制造单元的SCPN模型获得同构的马尔科夫链转移矩阵,将覆盖率和修复率加入可用性分析模型中,从而得到一种对FMS可用性进行评价的新方法。实验结果表明,所提方法可以对FMS进行建模,并能得到制造单元以及FMS的可用性,较以往的可用性评价方法更准确和全面。     

基于SHLPN的FMS建模及其分析技术的研究

FMS是一种结构复杂、投入巨大的系统,利用Petri网在建模方面的图形化、数学化特性可以准确地分析FMS系统的性能。本文结合一个柔性制造系统的具体实例,通过随机高级Petri网进行建模,利用随机高级Petri与随机马尔可夫链的同构关系及成熟的马尔可夫链理论,分析了FMS的多项性能指标。     

UML-OOPN集成建模方法及其在柔性制造系统的应用

针对柔性制造系统FMS(Flexible manufacturing system)建模特点,提出了UML-OOPN集成建模方法。该方法是用UML(the Unified Modeling Language)建立柔性制造系统FMS(Flexible manufacturing system)模型,然后将该模型映射为面向对象Petri网OOPN(Object-Oriented Petri Nets)网模型,通过Petri网模型的分析和仿真,其结果可用于修正和改进模型设计。     

基于Petri网的柔性制造系统调度控制模型

给出了自顶向下构造柔性制造系统(FMS)Petri网模型的方法,提出了随机创建指定数目满足指定条件的测试模型的算法,并给出程序仿真运行的演算规则。在此基础上实现了基于Petri网FMS分析与调度仿真软件平台,该平台可配置性强,容易维护,降低了柔性制造系统方针研究的复杂性。最后给出了该模型实现的模块结构。     

基于BP算法和Petri网的柔性制造系统故障诊断

针对传统Petri网难以精确描述故障发生的不确定性以及缺乏学习能力的缺点,将BP神经网络和加权模糊Petri网相结合,定义了一种新的能对故障进行诊断的模型——BPFPN网(A net based on BP and FPN),并提出了对BPFPN网故障诊断模型进行构造的算法,以及一种将BP神经网络算法应用于BPFPN网故障诊断模型实现对各种参数进行训练的方法;最后通过对实验参数为=5000,算法学习速率η=0.05,学习误差Δe=0.0002的柔性制造系统加工中心故障诊断实例进行实验,在对各种参数进行学习后,能够有效地实现对故障的诊断,证明了BPFPN网是一种有效的故障诊断方法。     

赋时离散事件系统的安全诊断

针对一类计时或非计时自动机模型,研究其赋时离散事件系统(TDES)故障诊断的安全性问题.首先对TDES的安全可诊断性进行形式化;然后通过构造一个非法语言识别器对被禁止危险操作序列进行识别,在此基础上构建一个安全诊断器,提出一种基于安全诊断器的安全诊断方法,并得到一个关于TDES安全可诊断性的充分必要条件,从而实现TDES的安全故障诊断.     

普适计算系统可用性度量

普适计算系统可用性度量具有典型的面向用户特点,为刻画用户行为需要引入具有非指数分布性质的用户状态,并且需要考虑用户态与系统态关联分析时的复杂情况。传统的连续时间马尔科夫链和半马尔科夫过程都不能很好地对以上情况给予分析。鉴于此,文章基于马尔科夫重生过程(MRGP)提出了普适计算系统的可用性度量方法。首先建立具有普适计算模式的用户模型,基于此构建了普适计算系统可用性度量的MRGP模型,对可用性进行了随机过程分析,给出了对应瞬态和稳态行为的度量方法,结合普适计算模式的特点定义了普适计算系统请求可用性度量。最后进行了数值分析,评价了普适计算系统可用性在MTTF和MTTR下的变化情况,据此导出来构建高可用普适计算系统需要考虑一些原则,并分析了用户和系统参数等因素对普适计算系统可用性度量的影响。     

基于EHLEP-N模型的FMS实时调度和控制

本文提出一种新的更适合柔性制造系统(FMS)建模的扩展高级E-Net,简称EHLEP-N (Extended High Level Evaluation Petri Net).将EHLEP-N与专家系统技术相结合,使 EHLEP-N对FMS更具有描述性,推理和决策能力.以EHLEP-N为FMS的建模工具,设 计并建立FMS实时调度控制专家系统.借助于该系统,提出并研究新的旨在减少空闲(noinputs) 和消除阻塞的实时动态再调度规则.加工实验结果表明:1)该系统满足实时性和调 度控制功能的要求;2)新规则的产率(throughputs)比传统规则平均提高7％.     

基于自适应神经网络加权模糊故障诊断研究

研究故障诊断问题;针对传统Petri网难以精确地描述故障现象和故障原因之间的复杂关系,基于模糊逻辑BP神经网络和传统Petri网模型结合,提出了一种新的自适应的加权模糊神经网络Petri网模型故障检测方法;该方法首先采用改进的BP神经网络算法对模型的权值进行训练,然后采用构造的自适应模糊Petri网模型对故障进行诊断;在柔性制造系统实例中进行了故障诊断,实验结果表明,该方法具有很强的故障推理能力以及自适应能力,能有效地对故障进行诊断,具有一定的实际应用价值。     

基于随机Petri网的可修系统可用性模糊评价

结合模糊集理论和随机Petri网理论提出了一种可修系统可用性建模与分析的新方法——模糊随机Petri网方法。随机Petri网的状态可达图同构于连续时间马尔可夫链，由可达图可得到系统的稳定状态概率方程组。利用模糊代数理论解该模糊方程组即可得到系统转移概率和各种性能指标的模糊数，通过解模糊可得到系统的可用性指标值。文章进行了实例分析并与已有文献作比较，举例进行分析求解，结果表明该方法是可行的。     

Performance analysis of limited number of wavelength converters by share per node in optical switching network

In this paper, we present studies of an optical switching (OS) node utilizing a limited number of WCs (wavelength converters) in order to reduce the implementation cost of an OS node. The study stems from practical observation that WCs are expensive. Consequently, each output wavelength may not necessarily have its own WC and has to share a limited pool of WCs with other output wavelengths. In order to improve the utilization of the limited number of WCs, a share per node (SPN) method is proposed for the OBS node. Subsequently, a multi-dimensional Markov chain model of SPN is presented to evaluate its performance. To reduce the complexity of the multi-dimension Markov analysis, we propose a suite of methods, called randomized states (RS) multi-plane Markov chain analysis, followed by self-constrained iteration (SCI) and eventually ending with the sliding window (SW) update method, to solve for the solution. Numerical results are presented to verify the accuracy of the analytical model. With SPN, about 50% and 80% of WCs can be saved in high load and low load scenarios respectively.     

Failure diagnosis and recovery based on DES framework

As many industrial systems become more complex, it becomes extremely difficult to diagnose the cause of failures. This paper presents a new failure diagnosis approach based on discrete-event systems (DES) framework. In particular, the approach is a hybrid of event-based and state-based ones leading to a simpler failure diagnoser with supervisory control capability. In our approach, we include the failure recovery events for failures in the system model in order to derive a diagnoser we refer to as a recoverable diagnoser. Further, in order to reduce the state size of the recoverable diagnoser, a procedure to construct a high-level diagnoser is presented. The design procedure for diagnoser is presented along with a pump-valve system as a illustrative example.     

A diagnoser algorithm for anomaly detection in DEDS under partial and unreliable observations: characterization and inclusion in sensor configuration optimization

Complex engineering systems have to be carefully monitored to meet demanding performance requirements, including detecting anomalies in their operations. There are two major monitoring challenges for these systems. The first challenge is that information collected from the monitored system is often partial and/or unreliable, in the sense that some occurred events may not be reported and/or may be reported incorrectly (e.g., reported as another event). The second is that anomalies often consist of sequences of event patterns separated in space and time. This paper introduces and analyzes a diagnoser algorithm that meets these challenges for detecting and counting occurrences of anomalies in engineering systems. The proposed diagnoser algorithm assumes that models are available for characterizing plant operations (via stochastic automata) and sensors (via probabilistic mappings) used for reporting partial and unreliable information. Methods for analyzing the effects of model uncertainties on the diagnoser performance are also discussed. In order to select configurations that reduce sensor costs, while satisfying diagnoser performance requirements, a sensor configuration selection algorithm developed in previous work is then extended for the proposed diagnoser algorithm. The proposed algorithms and methods are then applied to a multi-unit-operation system, which is derived from an actual facility application. Results show that the proposed diagnoser algorithm is able to detect and count occurrences of anomalies accurately and that its performance is robust to model uncertainties. Furthermore, the sensor configuration selection algorithm is able to suggest optimal sensor configurations with significantly reduced costs, while still yielding acceptable performance for counting the occurrences of anomalies.     

Centralized Modular Diagnosis and the Phenomenon of Coupling

This paper studies modular decomposition as an approach for failure diagnosis based on Discrete Event Systems. This paper also analyses the problem of coupling produced in the implementation of centralized modular diagnosers, as coupled diagnosers cannot carry out their own diagnosis task, when there is a failure in another subsystem sharing a common energy or material flow. In addition, we propose a method to avoid diagnoser coupling, by means of decoupling functions using non-local information with respect to the coupled diagnoser and generated in the diagnoser where the failure has been isolated.     

A polynomial algorithm for testing diagnosability of discrete-eventsystems

Failure diagnosis in large and complex systems is a critical task. In the realm of discrete-event systems, Sampath et al. (1995) proposed a language based failure diagnosis approach. They introduced the diagnosability for discrete-event systems and gave a method for testing the diagnosability by first constructing a diagnoser for the system. The complexity of this method of testing diagnosability is exponential in the number of states of the system and doubly exponential in the number of failure types. We give an algorithm for testing diagnosability that does not construct a diagnoser for the system, and its complexity is of fourth order in the number of states of the system and linear in the number of the failure types     

不完备离散事件系统的当前状态不透明性

本文针对不完备系统模型,研究不完备离散事件系统的当前状态不透明性.根据系统的实际输出与预测输出之间的差异,构建了一个具有学习功能的学习诊断器.这种学习诊断器不仅能够模拟系统的状态转移,而且还可以将系统缺失的状态信息通过学习得到恢复.通过引入集合覆盖理论处理由学习诊断器得出的结果,提出了一种基于学习诊断器的不完备离散事件系统当前状态不透明性的验证算法.     

基于BP网络的线性电路故障诊断

本文研究了基于神经网络的有容差线性电路故障诊断问题，提出了反向传播神经网络训练厉故障诊断器的具体方法。     

分布式功能块控制应用的性能分析

IEC61499功能块逐渐被工业采纳。本文针对分布式功能块控制应用（DFBCA）缺乏性能分析方法的情况，提出了一种基于随机Petri网的DFBCA性能分析方法。该方法以DFBCA的运行状态为着手点，利用Petri网易于表示系统中可能发生的各种状态变化及其关系的特点，将DFBCA转换为随机Petri网模型。再利用随机Petri网模型与马尔可夫链（MC）同构的特征，将随机Petri网模型转换为MC。得到的MC为DFBCA的性能分析提供了数学基础。最后基于MC的状态转移矩阵和稳态概率，对在每个状态中的驻留时间、变迁的利用率、变迁的标记流速、子系统延时时间等性能指标进行了分析。通过具体的示例说明了这种性能分析方法的可行性。