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81.
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。 相似文献
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现代战斗机的飞行包线范围较大,飞行条件严苛多变,机载电子设备的功率密度也在不断增加,为了保证飞行员安全舒适,保障电子设备可靠工作,空气循环系统的可靠性问题亟待解决.为此提出了一种战斗机空气循环系统的故障诊断方法,方法基于BP神经网络.首先,对空气循环系统进行故障分析,选取热交换器和涡轮为典型故障部件,以二者的效率为故障特征值.其次,对空气循环系统的四种典型故障模式进行仿真,由仿真得到的数据训练BP神经网络,实现故障模式识别.诊断结果表明,方法具有良好的准确性和自适应性,能够成功诊断空气循环系统的典型故障.针对战斗机空气循环系统提出的故障诊断方法合理,对系统的可靠性研究具有重要意义. 相似文献
85.
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.
Ben Smith is a second year Ph.D. student in Computer Science at North Carolina State University working as an RA under Dr. Laurie Williams. He received his Bachelor’s degree in Computer Science in May of 2007 and he hopes to receive his doctorate in 2012. He has begun work on developing SQL Coverage Metrics as a predictive measure of the security of a web application. This fall, he will be beginning the doctoral preliminary exam and working as a Testing Manager for the NCSU CSC Senior Design Center: North Carolina State’s capstone course for Computer Science. Finally, he has designed and maintained the websites for the Center for Open Software Engineering and ESEM 2009. Laurie Williams is an Associate Professor in the Computer Science Department of the College of Engineering at North Carolina State University. She leads the Software Engineering Reasearch group and is also the Director of the North Carolina State University Laboratory for Collaborative System Development and the Center for Open Software Engineering. She is also technical co-director of the Center for Open Software Engineering (COSE) and the area technical director of the Secure Open Systems Initiative (SOSI) at North Carolina State University. Laurie received her Ph.D. in Computer Science from the University of Utah, her MBA from Duke University, and her BS in Industrial Engineering from Lehigh University. She worked for IBM for nine years in Raleigh, NC before returning to academia. Laurie’s research interests include agile software development methodologies and practices, collaborative/pair programming, software reliability and testing, and software engineering for secure systems development. 相似文献
Laurie WilliamsEmail: |
Ben Smith is a second year Ph.D. student in Computer Science at North Carolina State University working as an RA under Dr. Laurie Williams. He received his Bachelor’s degree in Computer Science in May of 2007 and he hopes to receive his doctorate in 2012. He has begun work on developing SQL Coverage Metrics as a predictive measure of the security of a web application. This fall, he will be beginning the doctoral preliminary exam and working as a Testing Manager for the NCSU CSC Senior Design Center: North Carolina State’s capstone course for Computer Science. Finally, he has designed and maintained the websites for the Center for Open Software Engineering and ESEM 2009. Laurie Williams is an Associate Professor in the Computer Science Department of the College of Engineering at North Carolina State University. She leads the Software Engineering Reasearch group and is also the Director of the North Carolina State University Laboratory for Collaborative System Development and the Center for Open Software Engineering. She is also technical co-director of the Center for Open Software Engineering (COSE) and the area technical director of the Secure Open Systems Initiative (SOSI) at North Carolina State University. Laurie received her Ph.D. in Computer Science from the University of Utah, her MBA from Duke University, and her BS in Industrial Engineering from Lehigh University. She worked for IBM for nine years in Raleigh, NC before returning to academia. Laurie’s research interests include agile software development methodologies and practices, collaborative/pair programming, software reliability and testing, and software engineering for secure systems development. 相似文献
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María M. Seron Author Vitae Xiang W. Zhuo Author Vitae Author Vitae John J. Martínez Author Vitae 《Automatica》2008,44(1):88-97
In this paper we propose a novel fault tolerant multisensor switching strategy for feedback control. Each sensor of the proposed multisensor scheme has an associated state estimator which, together with a state feedback gain, is able to individually stabilise the closed-loop system. At each instant of time, the switching strategy selects the sensor-estimator pair that provides the best closed-loop performance, as measured by a control-performance criterion. We establish closed-loop stability of the resulting switching scheme under normal (fault-free) operating conditions. More importantly, we show that closed-loop stability is preserved in the presence of faulty sensors if a set of conditions on the system parameters (such as bounds on the sensor noises, maximum and minimum values of the reference signal, etc.) is satisfied. This result enhances and broadens the applicability of the proposed multisensor scheme since it provides guaranteed properties such as fault tolerance and robust closed-loop stability under sensor fault. The results are applied to the problem of automotive longitudinal control. 相似文献
88.
故障树分析是一种自上而下的方法,通过对可能造成系统故障的硬件、软件、环境、人为因素进行分析,分析得出故障原因的各种可能组合方式,由总体至部分,按树状结构,逐层细化的一种分析方法.开发了故障树分析工具,提供了对规范化故障树定性的割集计算、简化等功能,提出了基于容斥定理的定量化顶事件概率简化计算方法,可满足企业对部件级元件进行故障详细分析的需求.还给出了所开发的系统实现界面,系统已在某企业中得到了具体的应用. 相似文献
89.
This paper describes why the k-dimension maximal oriented energy subspace of the measurable voltage-change matrix id the optimal feature to locate faults in a population of circuits. The paper elaborately designs a "nearness" concept, which is used to construct a fault candidate set in a small size, and proposes a maximal nearness criterion. On the basis of these, the paper presents a novel algorithm to efficiently improve the accuracy and speed of fault locating. 相似文献
90.
As a representative deep learning network, Convolutional Neural Network (CNN) has been extensively used in bearing fault diagnosis and many good results have been reported. In Prognostics and Health Management (PHM) field, the CNN’s input size is usually designed as a 1D vector or 2D square matrix, and the convolution kernel size is also defined as a square shape like 3 × 3 and 5 × 5, which are directly adopted from the image recognition. Though satisfying results can be obtained, CNN with such parameter specifications is not optimal and efficient. To this end, this paper elaborated the physical characteristics of bearing acceleration signals to guide the CNN design. First, the fault period under different fault types and shaft rotation frequency were used to determine the size of CNN’s input. Next, an exponential function was involved in fitting the envelope of decaying acceleration signal during each fault period, and signal length within different decaying ratios was used to define the CNN’s kernel size. Finally, the designed CNN was validated with the Case Western Reserve University bearing dataset and Paderborn University bearing dataset. Results confirm that the physics-guided CNN (PGCNN) with rectangular input shape and rectangular convolution kernel works better than the baseline CNN with higher accuracy and smaller uncertainty. The feasibility of designing CNN parameters with physics-guided rules derived from bearing fault signal analysis has also been verified. 相似文献