Abstract. This paper describes a case study of modelbased diagnostics system development for an aircraft Auxiliary Power Unit (APU) turbine system. The offline diagnostics algorithms described in the paper work with historical data of a flight cycle. The diagnostics algorithms use detailed engine systems models and fault model knowledge available to Honeywell as the engine manufacturer. The developed algorithms provide fault condition estimates that allow for consistent detection of incipient performance faults and abnormal conditions.
Effective aerospace health management integrates component, subsystem and system level health monitoring strategies, consisting of anomaly/diagnostic/prognostic technologies, with an integrated modeling architecture that addresses failure mode mitigation and life cycle costs. Included within such health management systems will be various failure mode diagnostic and prognostic (D/P) approaches ranging from generic signal processing and experience-based algorithms to the more complex knowledge and model-based techniques. While signal processing and experienced-based approaches to D/P have proven effective in many applications, knowledge and model-based strategies can provide further improvements and are not necessarily more costly to develop or maintain. This paper will describe some generic prognostic and health management technical approaches to confidently diagnose the presence of failure modes or prognose a distribution on remaining time to failure. Specific examples of D/P strategies are presented herein that address valves, hot section lifmg and performance degradation of an Auxiliary Power Unit (APU) system. In addition, a model is presented for a Power Take Off (PTO) shaft and AMAD snout bearing.Keywords: Prognostics, Diagnostics, Aerospace Introduction:Various health monitoring technologies have been developed for aerospace applications that aid in the detection and classification of developing system faults. However, these technologies have traditionally focussed on fault detection and isolation within an individual subsystem. Health management system developers are just beginning to address the concepts of prognostics and the integration of anomaly, diagnostic and prognostic technologies across subsystems and systems. Hence, the ability to detect and isolate impending faults or to predict the future condition of a component or subsystem based on its current diagnostic state and available operating data is currently a high priority research topic. In addition, these technologies must be capable of communicating the root cause of a problem across subsystems and propagating the up/downstream effects across the health management architecture. This paper will introduce some generic prognostic and health management (PHM) system algorithmic approaches that are demonstrated within various aircraft subsystem components with the ability to predict the time to conditional or mechanical failure (on a real-time basis). Prognostic and health management systems that can effectively implement the capabilities presented herein offer a great opportunity in terms of reducing the overall Life Cycle Costs (LCC) of operating systems as well as decreasing the operations/maintenance logistics footprint.
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