Stakeholderoriented systematic design methodology for prognostic and health management system: Stakeholder expectation definition Abstract   Prognostic and health management (PHM) describes a set of capabilities that enable to detect anomalies, diagnose faults and predict remaining useful lifetime (RUL), leading to the effective and efficient maintenance and operation of assets such as aircraft. Prior research has considered the methodological factors of PHM system design, but typically, only one or a few aspects are addressed. For example, several studies address system engineering (SE) principles for application towards PHM design methodology, and a concept of requirements from a theoretical standpoint, while other papers present requirement specification and flowdown approaches for PHM systems. However, the state of the art lacks a systematic methodology that formulates all aspects of designing and comprehensively engineering a PHM system. Meanwhile, the process and specific implementation of capturing stakeholdersxe2x80x99 expectations and requirements are usually lacking details. To overcome these drawbacks, this paper proposes a stakeholderoriented design methodology for developing a PHM system from a systems engineering perspective, contributing to a consistent and reusable representation of the design. Further, it emphasizes the process and deployment of stakeholder expectations definition in detail, involving the steps of identifying stakeholders, capture their expectationsrequirements, and stakeholder and requirement analysis. Two case studies illustrate the applicability of the proposed methodology. The proposed stakeholderoriented design methodology enables the integration of the bespoke main tasks to design a PHM system, in which sufficient stakeholder involvement and consideration of their interests can lead to more precise and better design information. Moreover, the methodology comprehensively covers the aspects of traceability, consistency, and reusability to capture and define stakeholders and their expectations for a successful design.

BFM: a scalable and resourceaware method for adaptive mission planning of UAVs UAVs must continuously adapt their mission to face unexpected internal or external hazards. This paper proposes a new BFM model (Bayesian Networks built from FMEA tables for MDP). This scalable model offers a modular and comprehensive method to incorporate different types of diagnosis modules based on BN (Bayesian Networks) and FMEA table (Failure Mode and Effects Analysis) to mission specifications expressed as an MDP (Markov Decision Processes). The BFM model implements the complete decisionmaking process that covers both the application configurations at the embedded system level and the mission planning at the UAV level. These decisions are based on the QoS (Quality of Service) of applications, the resource use and the system and sensors health. We demonstrate on a case study for a target tracking mission that the BFM model can interface hazards and applications specifications and can improve the success and quality of the mission. To the best of our knowledge, this is the first proposal of a systematic method that integrates diagnosis modules to MDP model in order to take care of the implementation of embedded applications during a mission.