高可用群集多处理技术的原理、规划及配置

在金融、通信等服务领域必须保证关键性业务对外稳定、持续地进行,群集技术(双机)为此提供了可靠的保证.首先介绍了群集的概念,然后基于IBM的AIX系统的高可用群集多处理技术(hacmp for aiz),对群集的网络、管理软件、单点故障、资源组接管策略等进行了详细分析,最后以一个客户端连接到应用、应用连接到数据库的3层客户/服务器(C/S)结构的金融应用系统为例,对群集的规划及配置进行了比较详细的讨论,并给出互相接管模式的双机配置规划和相关应用及数据库的起停脚本.     

Testing Address Decoder Faults in Two-Port Memories: Fault Models, Tests, Consequences of Port Restrictions, and Test Strategy

A two-port memory contains two duplicated sets of address decoders, which operate independently. Testing such memories requires the use of single-port tests as well as special two-port tests; the test strategy determines which tests have to be used. Many two-port memories have ports which are read-only or write-only; this impacts the possible tests for single-port and two-port memories, as well as the test strategy. In this paper the effects of interference and shorts between the address decoders of the two ports on the fault modeling are investigated. Fault models and their tests are introduced. In addition, the consequences of the port restrictions (read-only or write-only ports) on the fault models and tests are discussed, together with the test strategy.     

电子设备故障诊断专家系统的设计

介绍了大型电子设备故障诊断专家系统的作用 ,阐述了该系统的软件设计、具体要求、功能及任务     

Extending Fault-Based Testing to Microelectromechanical Systems

As stable fabrication processes for MicroElectroMechanical Systems (MEMS) emerge, research efforts shift towards the design of systems of increasing complexity. The ways in which testing is going to be performed for large volume complex devices embedding MEMS are not known. As in the microelectronics industry, the development of cost-effective tests for larger systems may well require test stimuli targeting actual faults, developing fault lists and fault models for realistic manufacturing defects and failure modes, and using fault simulation as a major approach for assessing testability and dependability. In this paper, we illustrate how fault-based testing can be extended to MEMS, both for bulk and surface micromachining technologies, making possible the reuse of analog testing techniques.     

Enumerating catastrophic fault patterns in VLSI arrays with both uni- and bidirectional links

Characterization of catastrophic fault patterns (CFPs) and their enumeration have been studied by several authors. Given a linear array with a set of bypass links, an important problem is how to count the number of CFPs. Enumeration of CFPs for two link redundancy G={1,g} has been solved for both unidirectional and bidirectional link cases. In this paper, we consider the more general case of link redundancy G={1,2,…,k,g}, 2k<g. Using random walk as a tool, we enumerate CFPs for both unidirectional and bidirectional cases.     

A hierarchical test generation methodology for digital circuits

A new hierarchical modeling and test generation technique for digital circuits is presented. First, a high-level circuit model and a bus fault model are introduced—these generalize the classical gate-level circuit model and the single-stuck-line (SSL) fault model. Faults are represented by vectors allowing many faults to be implicitly tested in parallel. This is illustrated in detail for the special case of array circuits using a new high-level representation, called the modified pseudo-sequential model, which allows simultaneous test generation for faults on individual lines of a multiline bus. A test generation algorithm called VPODEM is then developed to generate tests for bus faults in high-level models of arbitrary combinational circuits. VPODEM reduces to standard PODEM if gate-level circuit and fault models are used. This method can be used to generate tests for general circuits in a hierarchical fashion, with both high- and low-level fault types, yielding 100 percent SSL fault coverage with significantly fewer test patterns and less test generation effort than conventional one-level approaches. Experimental results are presented for representative circuits to compare VPODEM to standard PODEM and to random test generation techniques, demonstrating the advantages of the proposed hierarchical approach.     

新疆富蕴县萨尔布拉克金矿预测区断裂特征及其控矿规律

在全面、系统收集和研究新疆富蕴县萨尔布拉克地区地质、地球物理、地球化学、矿产普查和科研资料的基础上，深入分析金矿成矿地质条件、控矿因素和找矿方法组合，对萨尔布拉克预测区95条断裂的地质、地球物理、地球化学特征进行了全面统计分析，确定了42条赋金断裂．其中早期断裂8条，占19％；晚期断裂34条，占81％．赋金断裂以晚期南北向为主，次为东西向和北西向．航磁异常向上延拓高度大者，即赋金断裂延深大者对金的成矿有利．     

Distributed diagnosis for homogeneous systems

This article presents a distributed fault-diagnosis algorithm for identifying faulty and fault-free units (processors, PEs, cells) in homogeneous systems. It is based on local comparison among units in a system and dissemination of the test results. Each unit performs comparison with its neighbors by using its own comparator. Unlike other approaches, the algorithm does not assume that diagnostic circuits are fault free. The algorithm is simple enough to be realized with small circuit overhead. The results are especially useful in locating faulty units in processor arrays implemented on a single chip or wafer. Computer simulation has shown that even for low unit yields, extremely high performance (fault coverage) can be obtained by adjusting algorithm parameters.     

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.     

A New Deep Dual Temporal Domain Adaptation Method for Online Detection of Bearings Early Fault

With the quick development of sensor technology in recent years, online detection of early fault without system halt has received much attention in the field of bearing prognostics and health management. While lacking representative samples of the online data, one can try to adapt the previously-learned detection rule to the online detection task instead of training a new rule merely using online data. As one may come across a change of the data distribution between offline and online working conditions, it is challenging to utilize the data from different working conditions to improve detection accuracy and robustness. To solve this problem, a new online detection method of bearing early fault is proposed in this paper based on deep transfer learning. The proposed method contains an offline stage and an online stage. In the offline stage, a new state assessment method is proposed to determine the period of the normal state and the degradation state for whole-life degradation sequences. Moreover, a new deep dual temporal domain adaptation (DTDA) model is proposed. By adopting a dual adaptation strategy on the time convolutional network and domain adversarial neural network, the DTDA model can effectively extract domain-invariant temporal feature representation. In the online stage, each sequentially-arrived data batch is directly fed into the trained DTDA model to recognize whether an early fault occurs. Furthermore, a health indicator of target bearing is also built based on the DTDA features to intuitively evaluate the detection results. Experiments are conducted on the IEEE Prognostics and Health Management (PHM) Challenge 2012 bearing dataset. The results show that, compared with nine state-of-the-art fault detection and diagnosis methods, the proposed method can get an earlier detection location and lower false alarm rate.