Mooring system diagnosis and structural reliability control for position moored vessels

Early diagnosis and fault-tolerant control are essential for safe operation of floating platforms where mooring systems maintain vessel position and must withstand environmental loads. This paper considers two critical faults, line breakage and loss of a buoyancy element and employs vector statistical change detection for timely diagnosis of faults. Diagnosis design is scrutinised and a procedure is proposed based on specified false alarm probability and estimation of the distribution of the test statistics on which change detection is based. A structural reliability index is applied for monitoring the safety level of each mooring line and a set-point chasing algorithm accommodates the effects of line failure, as an integral part of the reliability-based set-point chasing control algorithm. The feasibility of the diagnosis and of the fault-tolerant control strategy is verified in model basin tests.     

Exception handling analysis and transformation using fault injection: Study of resilience against unanticipated exceptions

ContextIn software, there are the error cases that are anticipated at specification and design time, those encountered at development and testing time, and those that were never anticipated before happening in production. Is it possible to learn from the anticipated errors during design to analyze and improve the resilience against the unanticipated ones in production?ObjectiveIn this paper, we aim at analyzing and improving how software handles unanticipated exceptions. The first objective is to set up contracts about exception handling and a way to assess them automatically. The second one is to improve the resilience capabilities of software by transforming the source code.MethodWe devise an algorithm, called short-circuit testing, which injects exceptions during test suite execution so as to simulate unanticipated errors. It is a kind of fault-injection techniques dedicated to exception-handling. This algorithm collects data that is used for verifying two formal contracts that capture two resilience properties w.r.t. exceptions: the source-independence and pure-resilience contracts. Then we propose a code modification technique, called “catch-stretching” which allows error-recovery code (of the form of catch blocks) to be more resilient.ResultsOur evaluation is performed on 9 open-source software applications and consists in analyzing 241 catch blocks executed during test suite execution. Our results show that 101/214 of them (47%) expose resilience properties as defined by our exception contracts and that 84/214 of them (39%) can be transformed to be more resilient.ConclusionOur work shows that it is possible to reason on software resilience by injecting exceptions during test suite execution. The collected information allows us to apply one source code transformation that improves the resilience against unanticipated exceptions. This works best if the test suite exercises the exceptional programming language constructs in many different scenarios.     

基于改进的无量纲指标信息融合故障诊断方法

针对无量纲指标信息的不确定会产生冲突证据的问题,提出一种基于改进证据理论的无量纲指标信息融合故障诊断方法.采用人工免疫算法构建原始证据;通过计算无量纲免疫检测器证据之间的相似度找出相似证据和冲突证据.保留相似证据,并利用可信度修正冲突证据,从而降低冲突证据对证据理论合成结果的影响.组合式旋转机械故障诊断试验验证本文方法有效.该方法能够明显提高故障诊断准确率.     

装备故障样本优化方法研究

测试性验证装备的故障样本往往相互关联,全部注入费用较高、代价较大.为了降低验证试验费用,采用适当方法对故障样本进行优化分析.为提高故障检测率,提出了一种等价样本的故障样本优化方法.方法在分析故障-测试关联矩阵及其扩展、故障模式功能等价集合和故障模式测试等价集合的基础上,构建了故障样本等价集合,并进行重要度特征分析和最大熵求解,确定了最小的故障样本集合.通过对某型试验台故障样本优化实例分析,并与传统的方法进行了试验结果对比分析,使得故障样本数量、试验费用大为减少,提高了测试性验证的经济性.     

汽车电控发动机故障诊断专家系统在高职教学中的应用

随着电子控制技术在汽车上的广泛应用,电控发动机的检修日益成为汽车维修的重点和难点,同时,发动机电控系统检修也是高职高专汽车类专业重要的专业课之一。对模糊理论和BP神经网络在故障诊断方面的应用进行了探讨,以BP神经网络为主,采用模糊理论作为有益补充设计了电控发动机故障诊断专家系统,给学生创造接近实际的虚拟学习环境,增强学生的学习积极性,取得了良好的教学效果。     

Fault tolerance in cellular automata at high fault rates

A commonly used model for fault-tolerant computation is that of cellular automata. The essential difficulty of fault-tolerant computation is present in the special case of simply remembering a bit in the presence of faults, and that is the case we treat in this paper. We are concerned with the degree (the number of neighboring cells on which the state transition function depends) needed to achieve fault tolerance when the fault rate is high (nearly 1/2). We consider both the traditional transient fault model (where faults occur independently in time and space) and a recently introduced combined fault model which also includes manufacturing faults (which occur independently in space, but which affect cells for all time). We also consider both a purely probabilistic fault model (in which the states of cells are perturbed at exactly the fault rate) and an adversarial model (in which the occurrence of a fault gives control of the state to an omniscient adversary). We show that there are cellular automata that can tolerate a fault rate 1/2−ξ (with ξ>0) with degree O((1/ξ²)log(1/ξ)), even with adversarial combined faults. The simplest such automata are based on infinite regular trees, but our results also apply to other structures (such as hyperbolic tessellations) that contain infinite regular trees. We also obtain a lower bound of Ω(1/ξ²), even with only purely probabilistic transient faults.     

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。