Diagnosability Analysis of a Class of Hierarchical State Machines

This paper addresses the problem of fault detection and isolation for a particular class of discrete event dynamical systems called hierarchical finite state machines (HFSMs). A new version of the property of diagnosability for discrete event systems tailored to HFSMs is introduced. This notion, called L₁-diagnosability, captures the possibility of detecting an unobservable fault event using only high level observations of the behavior of an HFSM. Algorithms for testing L₁-diagnosability are presented. In addition, new methodologies are presented for studying the diagnosability properties of HFSMs that are not L₁-diagnosable. These methodologies avoid the complete expansion of an HFSM into its corresponding flat automaton by focusing the expansion on problematic indeterminate cycles only in the associated extended diagnoser.

Multivariate statistical process control based on multiway locality preserving projections

An approach for multivariate statistical process control based on multiway locality preserving projections (LPP) is presented. The recently developed LPP is a linear dimensionality reduction technique for preserving the neighborhood structure of the data set. It is characterized by capturing the intrinsic structure of the observed data and finding more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. In this study, LPP is used to extract the intrinsic geometrical structure of the process data. Hotelling’s T² (D) and the squared prediction error (SPE or Q) statistic charts for on-line monitoring are then presented, and the contribution plots of these two statistical indices are used for fault diagnosis. Moreover, a moving window technique is used for the implementation of on-line monitoring. Case study was carried out with the data of industrial penicillin fed-batch cultivations. As a comparison, the results obtained with the MPCA are also presented. It is concluded that the Multiway LPP (MLPP) outperforms the conventional MPCA. Finally, the robustness of the MLPP monitoring is analyzed by adding noises to the original data.     

基于MAF模型的串扰时延故障的测试矢量生成

随着深亚微米技术,串扰噪声问题越来越严重。利用MAF模型的基本思想,探讨了一种串扰时延最大化算法,并且利用被修改的FAN算法,生成测试矢量。对于一条敏化通路,利用被修改的FAN算法适当地激活相应的攻击线和受害线,使电路在最恶劣情况下引起最大通路时延,从而实现更有效的时延测试。在标准电路ISCAS’85上进行实验验证,结果表明:该算法对于多攻击线的串扰时延故障的测试矢量产生是有效的。     

基于SVM和Kalman预测的非线性系统故障预报

针对Kalman预测在非线性系统故障预报中预测误差较大的问题.提出一种基于支持向量机预测新息的Kalman预测方法.根据未知非线性系统的典型变量分析子空间模型进行Kalman预测.采用支持向量机时间序列预测算法预测未来时刻的新息,利用新息进行Kalman单步和多步预报.在连续搅拌反应器上的仿真研究表明:所提出方法能准确地预测较长时间段内故障过程的劣化趋势,预知可能发生的故障,使操作人员有时间采取必要措施消除故障隐患.     

基于故障跟踪估计器的非线性时滞系统故障诊断

提出一种可有效检测和估计一类非线性时滞系统故障的故障跟踪估计器.根据预测控制和迭代学习控制的思想,在所选取的优化时域长度内,通过迭代算法调节故障跟踪估计器中的可调参数,使之逼近系统中实际发生的故障.与以往基于观测器的故障诊断方法不同的是,故障跟踪估计器可同时检测和估计系统中发生的故障,而且针对不同类型的故障亦有很好的适应性.仿真结果表明了所提出算法的可行性和有效性.     

隐核最小二乘分类器在故障诊断中的应用

作为一种基于正定核的学习方法,传统支持向量机(Support Vector Machine,SVM)能较好地解决小样本、非线性、过学习、维数灾和局部极小等问题,从而广泛应用于模式识别、回归估计等领域。当前,核方法及其在故障诊断中的应用引起了人们的广泛重视并成为研究热点。为解决传统支持向量对核函数正定性的限制及求解速度不高的缺陷,通过引入最小二乘支持向量机分类算法提高学习速度,采用隐核特征映射技术实现核函数的进一步扩展,提出了一种新的隐核最小二乘分类器(HKLSC)算法。将其应用于实际工业过程的故障诊断中并根据采集的滚动轴承数据进行了仿真。结果表明,该隐核分类器具有很好的故障诊断性能,为故障诊断提供了一种新的有效途径。     

基于微包协议的三模冗余容错计算机无缝重构算法

提出了一种基于微包传输协议的系统重构算法,制定了系统重构命令协议,设计了关键数据区的一致性管理算法.不需增加额外的硬件资源和系统开销,利用三机之间的数据交换通道,即可实现三模冗余容错计算机的无缝重构,保证系统在故障恢复重构时工作的连续性.同时,完善的重构命令协议使得重构软件的开发可以采用面向对象思想构成标准化的中间件,具有很强的扩展能力,为构建标准化嵌入式三模冗余容错计算机内核奠定了良好的技术基础.     

多轴转向车辆转向杆系的受力分析

某采用机械式液压助力转向的多轴转向车辆,经常出现转向杆系损坏的现象.为了分析转向杆系损坏的原因,进一步提高系统的可靠性,通过大型工程分析软件ADAMS对转向杆系进行虚拟样机建模.利用模型对转向杆系进行了虚拟试验分析,给出了各杆件受力分析的仿真结果.仿真结果分析表明造成转向杆系损坏的原因是由于受力过大,并提出了相应的改进措施.通过改进后测试,对虚拟样机的仿真结果进行了对比,证明虚拟样机模型是正确的,改进措施是有效的.结果使整个杆系最大受力降低了30%.     

On guiding the augmentation of an automated test suite via mutation analysis

Mutation testing has traditionally been used as a defect injection technique to assess the effectiveness of a test suite as represented by a “mutation score.” Recently, mutation testing tools have become more efficient, and industrial usage of mutation analysis is experiencing growth. Mutation analysis entails adding or modifying test cases until the test suite is sufficient to detect as many mutants as possible and the mutation score is satisfactory. The augmented test suite resulting from mutation analysis may reveal latent faults and provides a stronger test suite to detect future errors which might be injected. Software engineers often look for guidance on how to augment their test suite using information provided by line and/or branch coverage tools. As the use of mutation analysis grows, software engineers will want to know how the emerging technique compares with and/or complements coverage analysis for guiding the augmentation of an automated test suite. Additionally, software engineers can benefit from an enhanced understanding of efficient mutation analysis techniques. To address these needs for additional information about mutation analysis, we conducted an empirical study of the use of mutation analysis on two open source projects. Our results indicate that a focused effort on increasing mutation score leads to a corresponding increase in line and branch coverage to the point that line coverage, branch coverage and mutation score reach a maximum but leave some types of code structures uncovered. Mutation analysis guides the creation of additional “common programmer error” tests beyond those written to increase line and branch coverage. We also found that 74% of our chosen set of mutation operators is useful, on average, for producing new tests. The remaining 26% of mutation operators did not produce new test cases because their mutants were immediately detected by the initial test suite, indirectly detected by test suites we added to detect other mutants, or were not able to be detected by any test.

基于PCC控制的变频电牵引采煤机

该文从控制系统、硬件和软件设计三个方面着手,详细介绍了西安煤矿机械有限公司基于PCC开发的新型采煤机电气控制系统。