Estimation, Control, and Redundancy Management for Uncertain Networks of Cooperating Agents

In realistic network analysis, there are several uncertainties in the measurements and computation of the arcs and vertices. These uncertainties should also be considered in realizing the shortest path problem (SPP) due to the inherent fuzziness in the body of expert's knowledge. In this paper, we investigated the SPP under uncertainty to evaluate our modified genetic strategy. We improved the performance of genetic algorithm (GA) to investigate a class of shortest path problems on networks with vague arc weights. The solutions of the uncertain SPP with considering fuzzy path lengths are examined and compared in detail. As a robust metaheuristic, GA algorithm is modified and evaluated to tackle the fuzzy SPP (FSPP) with uncertain arcs. For this purpose, first, a dynamic operation is implemented to enrich the exploration/exploitation patterns of the conventional procedure and mitigate the premature convergence of GA technique. Then, the modified GA (MGA) strategy is used to resolve the FSPP. The attained results of the proposed strategy are compared to those of GA with regard to the cost, quality of paths and CPU times. Numerical instances are provided to demonstrate the success of the proposed MGA-FSPP strategy in comparison with GA. The simulations affirm that not only the proposed technique can outperform GA, but also the qualities of the paths are effectively improved. The results clarify that the competence of the proposed GA is preferred in view of quality quantities. The results also demonstrate that the proposed method can efficiently be utilized to handle FSPP in uncertain networks.

Download Full-text

Modeling the Propagation of Failures in Software Driven Hardware Systems to Enable Risk-Informed Design

Volume 16: Safety Engineering, Risk Analysis and Reliability Methods ◽

10.1115/imece2008-68861 ◽

2008 ◽

Cited By ~ 9

Author(s):

David C. Jensen ◽

Irem Y. Tumer ◽

Tolga Kurtoglu

Keyword(s):

Complex System ◽

Product Life Cycle ◽

System Modeling ◽

Software Component ◽

Temperature Data ◽

System Level ◽

Design Stage ◽

Redundancy Management ◽

Hardware System ◽

Failure Propagation

Software-driven hardware configurations account for the majority of modern complex systems. The often costly failures of such systems can be attributed to software specific, hardware specific, or software/hardware interaction failures. The understanding of the propagation of failures in a complex system is critical because, while a software component may not fail in terms of loss of function, a software operational state can cause an associated hardware failure. The least expensive phase of the product life cycle to address failures is during the design stage. This results in a need to evaluate how a combined software/hardware system behaves and how failures propagate from a design stage analysis framework. Historical approaches to modeling the reliability of these systems have analyzed the software and hardware components separately. As a result significant work has been done to model and analyze the reliability of either component individually. Research into interfacing failures between hardware and software has been largely on the software side in modeling the behavior of software operating on failed hardware. This paper proposes the use of high-level system modeling approaches to model failure propagation in combined software/hardware system. Specifically, this paper presents the use of the Function-Failure Identification and Propagation (FFIP) framework for system level analysis. This framework is applied to evaluate nonlinear failure propagation within the Reaction Control System Jet Selection of the NASA space shuttle, specifically, for the redundancy management system. The redundancy management software is a subset of the larger data processing software and is involved in jet selection, warning systems, and pilot control. The software component that monitors for leaks does so by evaluating temperature data from the fuel and oxidizer injectors and flags a jet as having a failure by leak if the temperature data is out of bounds for three or more cycles. The end goal is to identify the most likely and highest cost paths for fault propagation in a complex system as an effective way to enhance the reliability of a system. Through the defining of functional failure propagation modes and path evaluation, a complex system designer can evaluate the effectiveness of system monitors and comparing design configurations.

Download Full-text