Embedded Reasoning Supporting Aerospace IVHM

Abstract

The work presented in this paper summarizes an effort to advance the current state of vehicle health reasoning practice as a fundamental element of an Integrated Vehicle Health Management (IVHM) approach. This effort seeks to integrate detection, diagnostic, and prognostic capabilities with a hierarchical diagnostic reasoning architecture into a single synergistic, embeddable commercial off-the-shelf (COTS) unit. Three avenues have been pursued to advance the capabilities of health reasoning in support of IVHM. First, the scope of the systems/subsystems being monitored by the underlying Health and Usage Monitoring System (HUMS) has been expanded. The HUMS is comprised of subsystem specific prognostic and health management (PHM) modules developed to process raw measured data and supply condition indicators (CI) targeting several flight critical areas from advanced aerospace applications: such as electro-mechanical actuators (EMA), engine performance, power train vibration, and structural impact detection and isolation. Second, a hierarchical, model-based reasoning approach has been developed to isolate and classify latent failure mode manifestations existing in the condition indicators output by the diagnostic and prognostic algorithms and utilize this information to assess the functional availability of the vehicle and its constituent subsystems. At the lowest level, the embedded reasoning seeks to classify latent failure mode indications from raw sensor data or condition indicators considered as evidence sources and isolate the root cause mechanism along with the accompanying severity. In addition, a confidence assessment is determined by considering the temporal element and how many times a particular piece of indicting evidence has repeated in the previous N evaluation iterations. The mid-level of the reasoning architecture is employed to determine the overall functional availability of the constituent subsystems, i.e. what are the implications of the detected failure modes on the capability of the subsystem? At the highest level of reasoning provided, the functional availability assessments, or condition indicators, from all underlying subsystems are utilized in conjunction with any applicable remaining useful life assessments to facilitate informed decisions about whether or not the requirements of scheduled flight operations can be fulfilled. Additionally, the temporal element is again exploited to determine the existence of a failure propagation path and isolate the most likely root cause failure mode. The final avenue is a demonstration on a COTS platform, built upon the PC-104 data bus architecture that provides a solid data acquisition and processing foundation. The work presented here highlights an end–to–end approach for addressing mission-critical subsystems across an advanced aerospace platform, from data acquisition to communication of intelligently reasoned root-cause failure mode and functional availability assessments made at the subsystem, system, and vehicle levels. Integration of this technology is believed to greatly support and enhance the efficacy of an IVHM system in managing the operation and maintenance of the host platform, facilitating proactive management, reducing operating costs, and improving operational availability.