Spot core from the San Andreas Fault Observatory at Depth (SAFOD) borehole provides the opportunity to characterize and quantify damage and mineral alteration of siliciclastics within an active, large-displacement plate-boundary fault zone. Deformed arkosic, coarse-grained, pebbly sandstone, and fine-grained sandstone and siltstone retrieved from 2.55 km depth represent the western damaged zone of the San Andreas Fault, approximately 130 m west of the Southwest Deforming Zone (SDZ). The sandstone is cut by numerous subsidiary faults that display extensive evidence of repeating episodes of compaction, shear, dilation, and cementation. The subsidiary faults are grouped into three size classes: 1) small faults, 1 to 2 mm thick, that record an early stage of fault development, 2) intermediate-size faults, 2 to 3 mm thick, that show cataclastic grain size reduction and flow, extensive cementation, and alteration of host particles, and 3) large subsidiary faults that have cemented cataclastic zones up to 10 mm thick. The cataclasites contain fractured host-rock particles of quartz, oligoclase, and orthoclase, in addition to albite and laumontite produced by syn-deformation alteration reactions. Five structural units are distinguished in the subsidiary fault zones: fractured sandstones, brecciated sandstones, microbreccias, microbreccias within distinct shear zones, and principal slip surfaces. We have quantified the particle size distributions and the particle shape of the host rock mineral phases and the volume fraction of the alteration products for these representative structural units. Shape characteristics vary as a function of shear strain and grain size, with smooth, more circular particles evolving as a result of increasing shear strain. Overall, the particle sizes are consistent with a power law distribution over the particle size range investigated (0.3 µm < d < 400 µm). The exponent (fractal dimension, D) is found to increase with shear strain and volume fraction of laumontite. This overall increase in D and evolution of shape with increasing shear strain reflects a general transition from constrained comminution, active at low shear strains to abrasion processes that dominate at high shear strains.

This dissertation presents a model-based reasoning architecture with a two fold purpose: to detect and classify component faults from observable system behavior, and to generate fault propagation models so as to make a more accurate estimation of current operational risks. It incorporates a novel approach to system level diagnostics by addressing the need to reason about low-level inaccessible components from observable high-level system behavior. In the field of complex system maintenance it can be invaluable as an aid to human operators. The first step is the compilation of the database of functional descriptions and associated fault-specific features for each of the system components. The system is then analyzed to extract structural information, which, in addition to the functional database, is used to create the structural and functional models. A fault-symptom matrix is constructed from the functional model and the features database. The fault threshold levels for these symptoms are founded on the nominal baseline data. Based on the fault-symptom matrix and these thresholds, a diagnostic decision tree is formulated in order to intelligently query about the system health. For each faulty candidate, a fault propagation tree is generated from the structural model. Finally, the overall system health status report includes both the faulty components and the associated at risk components, as predicted by the fault propagation model.

A diminuição no custo de computadores pessoais tem favorecido a construção de sistemas computacionais complexos, tais como aglomerados e grades. Devido ao grande número de recursos existentes nesses sistemas, a probabilidade de que faltas ocorram é alta. Uma abordagem que auxilia a tornar sistemas mais robustos na presença de faltas é a detecção de sua ocorrência, a fim de que processos possam ser reiniciados em estados seguros, ou paralisados em estados que não ofereçam riscos. Abordagens comumente adotadas para detecção seguem, basicamente, três tipos de estratégias: as baseadas em mensagens de controle, em estatística e em aprendizado de máquina. No entanto, elas tipicamente não consideram o comportamento de processos ao longo do tempo. Observando essa limitação nas pesquisas relacionadas, este trabalho apresenta uma abordagem para medir a variação no comportamento de processos ao longo do tempo, a fim de que mudanças inesperadas sejam detectadas. Essas mudanças são consideradas, no contexto deste trabalho, como faltas, as quais representam transições indesejadas entre estados de um processo e podem levá-lo a processamento incorreto, fora de sua especificação. A proposta baseia-se na estimação de cadeias de Markov que representam estados visitados por um processo durante sua execução. Variações nessas cadeias são utilizadas para identificar faltas. A abordagem proposta é comparada à técnica de aprendizado de máquina Support Vector Machines, bem como à técnica estatística Auto-Regressive Integrated Moving Average. Essas técnicas foram escolhidas para comparação por estarem entre as mais empregadas na literatura. Experimentos realizados mostraram que a abordagem proposta possui, com erro \'alfa\' = 1%, um F-Measure maior do que duas vezes o alcançado pelas outras técnicas. Realizou-se também um estudo adicional de predição de faltas. Nesse sentido, foi proposta uma técnica preditiva baseada na reconstrução do comportamento observado do sistema. A avaliação da técnica mostrou que ela pode aumentar em até uma ordem de magnitude a disponibilidade (em horas) de um sistema / The cost reduction for personal computers has enabled the construction of complex computational systems, such as clusters and grids. Because of the large number of resources available on those systems, the probability that faults may occur is high. An approach that helps to make systems more robust in the presence of faults is their detection, in order to restart or stop processes in safe states. Commonly adopted approaches for detection basically follow one of three strategies: the one based on control messages, on statistics or on machine learning. However, they typically do not consider the behavior of processes over time. Observing this limitation in related researches, this work presents an approach to measure the level of variation in the behavior of processes over time, so that unexpected changes are detected. These changes are considered, in the context of this work, as faults, which represent undesired transitions between process states and may cause incorrect processing, outside the specification. The approach is based on the estimation of Markov Chains that represent states visited by a process during its execution. Variations in these chains are used to identify faults. The approach is compared to the machine learning technique Support Vector Machines, as well as to the statistical technique Auto-Regressive Integrated Moving Average. These techniques have been selected for comparison because they are among the ones most employed in the literature. Experiments conducted have shown that the proposed approach has, with error \'alpha\'= 1%, an F-Measure higher than twice the one achieved by the other techniques. A complementary study has also been conducted about fault prediction. In this sense, a predictive approach based on the reconstruction of system behavior was proposed. The evaluation of the technique showed that it can provide up to an order of magnitude greater availability of a system in terms of uptime hours

The increasing numbers and complexity of spacecraft is driving a growing need for automated fault detection, isolation, and recovery. Anomalies and failures are common occurrences during space flight operations, yet most spacecraft currently possess limited ability to detect them, diagnose their underlying cause, and enact an appropriate response. This leaves ground operators to interpret extensive telemetry and resolve faults manually, something that is impractical for large constellations of satellites and difficult to do in a timely fashion for missions in deep space. A traditional hurdle for achieving autonomy has been that effective fault detection, isolation, and recovery requires appreciating the wider context of telemetry information. Advances in machine learning are finally allowing computers to succeed at such tasks. This dissertation presents an architecture based on machine learning for detecting, diagnosing, and responding to faults in a spacecraft attitude determination and control system. Unlike previous approaches, the availability of faulty examples is not assumed. In the first level of the system, one-class support vector machines are trained from nominal data to flag anomalies in telemetry. Meanwhile, a spacecraft simulator is used to model the activation of anomaly flags under different fault conditions and train a long short-term memory neural network to convert time-dependent anomaly information into a diagnosis. Decision theory is then used to convert diagnoses into a recovery action. The overall technique is successfully validated on data from the LightSail 2 mission. <br>

Recent refinements in Industry 4.0 and Machine Learning demonstrate the positive effects of using deep learning models for intelligent maintenance. The primary benefit of Deep Learning (DL) is its capability to extract attributes and make fast, accurate, and automated predictions without supervision. However, DL requires high computational power, significant data preprocessing, and vast amounts of data to make accurate predictions for intelligent maintenance. Given the considerable obstacles, meta-learning has been developed as a novel way to overcome these challenges. As a learning technique, meta-learning aims to quickly acquire knowledge of new tasks using theminimal available data by learning through meta-knowledge. There has been less research in the area of using meta-learning for Predictive Maintenance (PdM) and we considered it necessary to conduct this review to understand the applicability of meta-learning’s capabilities and functions to PdM since the outcomes of this technique seem to be rather promising. The review started with the development of a methodology and four research questions: (1) What is the taxonomy of meta-learning for PdM?, (2) What are the current state-of-the-art methodologies? (3) Which datasets are available for meta-learning in PdM?, and (4) What are the open issues, challenges, and opportunities of meta-learning in PdM?. To answer the first and second questions, a new taxonomy was proposed and meta-learnings role in predictive maintenance was identified from selected 55 papers. To answer the third question, we determined which types of datasets and their characteristics exist for this domain. Finally, the challenges, open issues, and opportunities of meta-learning in predictive maintenance were examined to answer the final question. The results of the research questions provided suggestions for future research topics.

The test of digital integrated circuits compares the test pattern results for the device under test (DUT) to the expected test pattern results of a standard reference. The standard response is typically obtained from simulations. The test pattern and response are created and evaluated assuming ideal test conditions. The standard response is normally stored within automated test equipment (ATE). However the use of ATE is the major contributor to the test cost. This thesis explores an alternative strategy to the standard response. As an alternative to the stored standard response, the response is estimated by fault tolerant technique. The purpose of the fault tolerant technique is to eliminate the need of standard response and enable online/real-time testing. Fault tolerant techniques use redundancy and majority voting to estimate the standard response. Redundancy in the circuit leads to fault aliasing. Fault aliasing misleads the majority voter in estimating the standard response. The statistics and phenomenon of aliasing are analyzed for benchmark circuits. The impact of fault aliasing on test with respect to coverage, test escape and over-kill is analyzed. The results show that aliasing can be detected with additional test vectors and get 100% fault coverage.

No description available.

No description available.

The popularity of MPI-based middleware and applications has led to their wide deployment. Such systems, however, are not inherently reliable and cannot tolerate external faults. This thesis presents a novel model-based approach for exploiting application features and other characteristics to categorize and create AEMs (Application Execution Model). This work realizes MPI/FT(tm), a middleware derived by selective incorporation of fault-tolerant features into MPI/Pro(tm) for two relevant AEMs. This thesis proves the following hypothesis: it is possible to successfully complete select MPI applications even in the presence of external faults, and such fault-tolerance can be achieved with acceptable performance overhead. This work defines parameters to measure the impact of this middleware on performance through faultree and fault-injected overheads. The hypothesis is validated through experimentation and measurement of sample MPI applications for two AEMs.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / This paper proposes a problem that the error tolerance property of spread spectrum sequence influences the performance of spread spectrum system. Then the relation is analyzed between the error tolerance property and the correlation property of binary sequence when correlation detection is proceeded, and the theoretical limitation of error tolerance is given. Finally, we investigate the relationship between the determination of the output decision threshold of correlation, the probability of correlation peak detection and the error tolerance of the spread spectrum sequence.