14,432 research outputs found

    Software that Learns from its Own Failures

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    All non-trivial software systems suffer from unanticipated production failures. However, those systems are passive with respect to failures and do not take advantage of them in order to improve their future behavior: they simply wait for them to happen and trigger hard-coded failure recovery strategies. Instead, I propose a new paradigm in which software systems learn from their own failures. By using an advanced monitoring system they have a constant awareness of their own state and health. They are designed in order to automatically explore alternative recovery strategies inferred from past successful and failed executions. Their recovery capabilities are assessed by self-injection of controlled failures; this process produces knowledge in prevision of future unanticipated failures

    Data-driven Soft Sensors in the Process Industry

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    In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

    Knowledge-based diagnosis for aerospace systems

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    The need for automated diagnosis in aerospace systems and the approach of using knowledge-based systems are examined. Research issues in knowledge-based diagnosis which are important for aerospace applications are treated along with a review of recent relevant research developments in Artificial Intelligence. The design and operation of some existing knowledge-based diagnosis systems are described. The systems described and compared include the LES expert system for liquid oxygen loading at NASA Kennedy Space Center, the FAITH diagnosis system developed at the Jet Propulsion Laboratory, the PES procedural expert system developed at SRI International, the CSRL approach developed at Ohio State University, the StarPlan system developed by Ford Aerospace, the IDM integrated diagnostic model, and the DRAPhys diagnostic system developed at NASA Langley Research Center

    A Pattern Language for High-Performance Computing Resilience

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    High-performance computing systems (HPC) provide powerful capabilities for modeling, simulation, and data analytics for a broad class of computational problems. They enable extreme performance of the order of quadrillion floating-point arithmetic calculations per second by aggregating the power of millions of compute, memory, networking and storage components. With the rapidly growing scale and complexity of HPC systems for achieving even greater performance, ensuring their reliable operation in the face of system degradations and failures is a critical challenge. System fault events often lead the scientific applications to produce incorrect results, or may even cause their untimely termination. The sheer number of components in modern extreme-scale HPC systems and the complex interactions and dependencies among the hardware and software components, the applications, and the physical environment makes the design of practical solutions that support fault resilience a complex undertaking. To manage this complexity, we developed a methodology for designing HPC resilience solutions using design patterns. We codified the well-known techniques for handling faults, errors and failures that have been devised, applied and improved upon over the past three decades in the form of design patterns. In this paper, we present a pattern language to enable a structured approach to the development of HPC resilience solutions. The pattern language reveals the relations among the resilience patterns and provides the means to explore alternative techniques for handling a specific fault model that may have different efficiency and complexity characteristics. Using the pattern language enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack.Comment: Proceedings of the 22nd European Conference on Pattern Languages of Program

    Making intelligent systems team players: Case studies and design issues. Volume 1: Human-computer interaction design

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    Initial results are reported from a multi-year, interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. The objective is to achieve more effective human-computer interaction (HCI) for systems with real time fault management capabilities. Intelligent fault management systems within the NASA were evaluated for insight into the design of systems with complex HCI. Preliminary results include: (1) a description of real time fault management in aerospace domains; (2) recommendations and examples for improving intelligent systems design and user interface design; (3) identification of issues requiring further research; and (4) recommendations for a development methodology integrating HCI design into intelligent system design

    Damage Tolerant Active Contro l: Concept and State of the Art

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    Damage tolerant active control is a new research area relating to fault tolerant control design applied to mechanical structures. It encompasses several techniques already used to design controllers and to detect and to diagnose faults, as well to monitor structural integrity. Brief reviews of the common intersections of these areas are presented, with the purpose to clarify its relations and also to justify the new controller design paradigm. Some examples help to better understand the role of the new area

    Machine learning and its applications in reliability analysis systems

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    In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA
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