An efficient phased mission reliability analysis for autonomous vehicles

Autonomous systems are becoming more commonly used, especially in hazardous situations. Such systems are expected to make their own decisions about future actions when some capabilities degrade due to failures of their subsystems. Such decisions are made without human input, therefore they need to be well-informed in a short time when the situation is analysed and future consequences of the failure are estimated. The future planning of the mission should take account of the likelihood of mission failure. The reliability analysis for autonomous systems can be performed using the methodologies developed for phased mission analysis, where the causes of failure for each phase in the mission can be expressed by fault trees. Unmanned Autonomous Vehicles (UAVs) are of a particular interest in the aeronautical industry, where it is a long term ambition to operate them routinely in civil airspace. Safety is the main requirement for the UAV operation and the calculation of failure probability of each phase and the overall mission is the topic of this paper. When components or sub-systems fail or environmental conditions throughout the mission change, these changes can affect the future mission. The new proposed methodology takes into account the available diagnostics data and is used to predict future capabilities of the UAV in real-time. Since this methodology is based on the efficient BDD method, the quickly provided advice can be used in making decisions. When failures occur appropriate actions are required in order to preserve safety of the autonomous vehicle. The overall decision making strategy for autonomous vehicles is explained in this paper. Some limitations of the methodology are discussed and further improvements are presented based on experimental results

Relationships Between Archimedean Copulas and Morgenstern Utility Functions

The (additive) generator of an Archimedean copula is a strictly decreasing and convexfunction, while Morgenstern utility functions (applying to risk aversion decision makers) arenondecreasing and concave. In this presentation, relationships between generators and utilityfunctions are established. For some well known Archimedean copula families, links betweenthe generator and the corresponding utility function are demonstrated.Some new copulafamilies are derived from classes of utility functions which appeared in the literature, andtheir properties are discussed. It is shown how dependence properties of an Archimedeancopula translate into properties of the utility function from whichthey are constructed

Reliability analysis of phased missions

In a phased mission the relevant system configuration (block diagram or fault tree) changes during consecutive time periods (phases). Many systems are required to perform phased missions. A classic example is a space vehicle. A reliability analysis for a phased mission encounters complexities not present with just one phase, but can be transformed into an analysis of a synthetic single phase case. The transformation has a potential for direct application, or can be used to study various computational algorithms and approximations.Office of Naval Research and Strategic Systems Project Office61153N, RR014-05-01, NR042-300, WR5-0017Approved for public release; distribution is unlimited

The Effect of Modeling Depth on Reliability Prediction for Systems Subject to a Phased Mission Profile

The term phased mission profile describes a situation in which the factors that influence the longevity of a system change in the course of a sequence of distinct, successive periods of time which are the mission phases. Phased mission profile tend to be associated with more general phased missions, in which there can also be changes in the system configuration that is relevant to mission success, but many systems with a stable configuration are exposed to phased mission profiles. Predictions of the probability of mission success for a system typically result from combining predicted probabilities of mission success for its components according to a logic model for system's configuration. We investigate the effect that the depth to which the logic model is carried has on predictions, which the predictions at the component level are made using a standard methodology.Approved for public release; distribution is unlimited

Some classes of distributions closed under the formation of coherent systems

Technical Report No. 59It has been shown by Birhbaum, Esary and Marshall that the class of survival functions with increasing hazard rate average (IHRA) is closed under the formation of coherent systems. Moreover, this is the smallest class of survival functions which is closed both under the formation of coherent system and limits in distribution, and which contains the exponential survival functions. In this paper a number of other classes are found which are closed under the formation of coherent systems and limits in distribution. Associated subclasses that play a generating role like the exponential class in the IHRA case are exhibited. In addition, several methods are presented for deriving closed classes from closed classes.Office of Naval ResearchN00014-67-A-0103-0015Approved for public release; distribution is unlimited

Reliability Analysis of Phased Missions

In a phased mission the relevant system configuration (block diagram or fault tree) changes during consecutive time periods (phases). Many systems are required to perform phased missions. A classic example is a space vehicle. A reliability analysis for a phased mission encounters complexities not present with just one phase, but can be transformed into an analysis of a synthetic single phase case. The transformation has a potential for direct application, or can be used to study various computational algorithms and approximations.Office of Naval Research and Strategic Systems Project Office61153N, RR014-05-01, NR042-300, WR5-0017Approved for public release; distribution is unlimited