The Phantom Works' role at The Boeing Company is to facilitate the development and transition of emerging technologies into Boeing products to provide a competitive advantage. 12 Phantom Works is developing a Health Management Engineering Environment (HMEE) to foster and transition health management technology from industry, academia and government technology base to Boeing Products. The HMEE consists of a:• Program Analysis and Modeling Environment, • Development Environment and an • Operations Environment.The Program Analysis and Modeling Environment provides the processes and tools to do performance/cost driver analysis, root cause analysis, solution formulation, and cost/benefit trade studies including fleet simulation modeling. The Development Environment provides the design processes and synthesis tools required to develop solutions.The Operations Environment provides the processes and tools to integrate, test and mature the hardware and software elements of the health management solution. Major elements of the HMEE are an open software reference architecture, access to hardware laboratories to characterize degraded components/ subsystems, a data repository to store and access laboratory and field data, and hardware and software to support end to end technology demonstrations. It will support progressive levels of integration and demonstration from a single technology on a PC to the integration of this technology into a complete IVHM system with hardware in the loop as needed to provide vehicle data and address integration into vehicle avionics or a ground support system. Significant elements of the HMEE are in place with additional expansion and integration ongoing. The Boeing Phantom Works IVHM team is acquiring hardware, tools and algorithms from a number of suppliers to create a pool of resources for system level, end to end demonstrations of IVHM applications and development tools.
This paper describes how a Timed Failure Propagation Graph (TFPG) based reasoner can be used to optimally diagnose failures in a complex real time system, such as an aerospace vehicle. The TFPG approach is a specific instantiation of model based diagnostic reasoning which has been well documented [1].The authors have been collaborating over the last several years to identify the practical issues that are associated with the application of the TFPG reasoner for real time fault isolation in a large scale, dynamically changing environment; the resulting modifications to the reasoner have also been previously documented [2, 3].The focus of this paper is to illustrate, using a notional but representative fuel system case study, how the TFPG reasoner can efficiently and effectively diagnose both single and multiple failure scenarios.Using actual results obtained for this example, we will explore the practical implementation challenges that would typically be encountered when building a diagnostic reasoner implementation, and then demonstrate the TFPG features that have been implemented to address these issues.
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