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Health Ready Components are essential to unlocking the potential of Integrated Vehicle Health Management (IVHM) as it relates to real-time diagnosis and prognosis in order to achieve lower maintenance costs, greater asset availability, reliability and safety. IVHM results in reduced maintenance costs by providing more accurate fault isolation and repair guidance. IVHM results in greater asset availability, reliability and safety by recommending preventative maintenance and by identifying anomalous behavior indicative of degraded functionality prior to detection of the fault by other detection mechanisms. The cost, complexity and effectiveness of the IVHM system design, deployment and support depend, to a great extent, on the degree to which components and subsystems provide the run-time data needed by IVHM and the design time semantic data to allow IVHM to interpret those messages.

Electrical power generation is an important component in the Electrical Power System of an Aircraft (EPS). In this paper we present a model based diagnostic framework for early generator degradation detection and tracking within an Aircraft Generator. The nominal health state is modeled empirically using an Auto-associative Kernel Regression (AAKR) technique using signals extracted from a healthy generator. Later the health state is estimated by comparing sensor observations with the model predictions. Finally, a Sequential Probability Ratio Test (SPRT) is used to detect and track degradation. This model based framework showed excellent degradation tracking performance when it was tested on a unit that was run to failure.

This paper describes a framework for developing an Integrated Electrical Power System (EPS) Health Management System. The framework is based on the perspective that health management, unlike other capabilities, is not a self-contained, stand-alone system, but is rather an integral part of every aircraft subsystem, system, and the entire platform. Ultimately, the objective is to improve the entire maintenance, logistics and fleet operations support processes. This perspective requires a new mindset when applying systems engineering design principles. The paper provides an overview description of the framework, the potential benefits of the approach and some critical design and implementation issues based on current development efforts.

This paper describes a novel time-varying prognostic modeling framework for computing condition-based residual life distributions of partially degraded aircraft electrical power system (EPS) components. This advanced methodology is suitable for modeling the evolution of degradation signals acquired from aircraft electrical power system components through condition monitoring techniques. The evolution of the degradation signals is modeled as a stochastic process, with fixed and random parameters, and takes into consideration environmental covariates that capture the time-varying nature of the equipment's operating condition. The modeling methodology correlates the degradation signals with the underlying physical transitions that occur prior to component failures to estimate and update the residual life distribution of the system.