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%.     

战斗机空气循环系统故障诊断方法的仿真研究

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.     

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。     

Multisensor switching control strategy with fault tolerance guarantees

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.     

.NET平台下故障树分析技术的实现及应用

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.     

Oriented energy,nearness and fault diagnosis

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.     

CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis

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.