24,429 research outputs found
Adaptive Signal Processing Strategy for a Wind Farm System Fault Accommodation
In order to improve the availability of offshore wind farms, thus avoiding unplanned operation and maintenance costs, which can be high for offshore installations, the accommodation of faults in their earlier occurrence is fundamental. This paper addresses the design of an active fault tolerant control scheme that is applied to a wind park benchmark of nine wind turbines, based on their nonlinear models, as well as the wind and interactions between the wind turbines in the wind farm. Note that, due to the structure of the system and its control strategy, it can be considered as a fault tolerant cooperative control problem of an autonomous plant. The controller accommodation scheme provides the on-line estimate of the fault signals generated by nonlinear filters exploiting the nonlinear geometric approach to obtain estimates decoupled from both model uncertainty and the interactions among the turbines. This paper proposes also a data-driven approach to provide these disturbance terms in analytical forms, which are subsequently used for designing the nonlinear filters for fault estimation. This feature of the work, followed by the simpler solution relying on a data-driven approach, can represent the key point when on-line implementations are considered for a viable application of the proposed scheme
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Protective wrapping of off-the-shelf components
System designers using off-the-shelf components (OTSCs), whose internals they cannot change, often use add-on “wrappers” to adapt the OTSCs’ behaviour as required. In most cases, wrappers are used to change “functional” properties of the components they wrap. In this paper we discuss instead protective wrapping, the use of wrappers to improve the dependability – i.e., “non-functional” properties like availability, reliability, security, and/or safety – of a component and thus of a system. Wrappers can improve dependability by adding fault tolerance, e.g. graceful degradation, or error recovery mechanisms. We discuss the rational specification of such protective wrappers in view of system dependability requirements, and highlight some of the design trade-offs and uncertainties that affect system design with OTSCs and wrappers, and that differentiate it from other forms of fault-tolerant design
A Pattern Language for High-Performance Computing Resilience
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
On design of robust fault detection filter in finite-frequency domain with regional pole assignment
This brief is concerned with the fault detection (FD) filter design problem for an uncertain linear discrete-time system in the finite-frequency domain with regional pole assignment. An optimized FD filter is designed such that: 1) the FD dynamics is quadratically D-stable; 2) the effect from the exogenous disturbance on the residual is attenuated with respect to a minimized H∞-norm; and 3) the sensitivity of the residual to the fault is enhanced by means of a maximized H--norm. With the aid of the generalized Kalman-Yakubovich-Popov lemma, the mixed H--/H∞ performance and the D-stability requirement are guaranteed by solving a convex optimization problem. An iterative algorithm for designing the desired FD filter is proposed by evaluating the threshold on the generated residual function. A simulation result is exploited to illustrate the effectiveness of the proposed design technique.This work was supported in part by the Deanship of Scientific Research (DSR) at King Abdulaziz University in Saudi Arabia under Grant 16-135- 35-HiCi, the National Natural Science Foundation of China under Grants
61134009 and 61203139, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany
Distributed L1-state-and-fault estimation for Multi-agent systems
In this paper, we propose a distributed state-and-fault estimation scheme for
multi-agent systems. The proposed estimator is based on an -norm
optimization problem, which is inspired by sparse signal recovery in the field
of compressive sampling. Two theoretical results are given to analyze the
correctness of the proposed approach. First, we provide a necessary and
sufficient condition such that state and fault signals are correctly estimated.
The result presents a fundamental limitation of the algorithm, which shows how
many faulty nodes are allowed to ensure a correct estimation. Second, we
provide a sufficient condition for the estimation error of fault signals when
numerical errors of solving the optimization problem are present. An
illustrative example is given to validate the effectiveness of the proposed
approach
Interacting Components
SystemCSP is a graphical modeling language based on both CSP and concepts of component-based software development. The component framework of SystemCSP enables specification of both interaction scenarios and relative execution ordering among components. Specification and implementation of interaction among participating components is formalized via the notion of interaction contract. The used approach enables incremental design of execution diagrams by adding restrictions in different interaction diagrams throughout the process of system design. In this way all different diagrams are related into a single formally verifiable system. The concept of reusable formally verifiable interaction contracts is illustrated by designing set of design patterns for typical fault tolerance interaction scenarios
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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