Development of False Alarm Mitigation Techniques
Small Business Information
INTELLIGENT AUTOMATION CORP.
13029 Danielson Street, Suite 200, Poway, CA, 92064
AbstractAn essential premise motivating the Joint Strike Fighter Prognostics and Health Management (JSF-PHM) approach is the capability of correctly detecting faults in components or subsystems. Whether a fault is declared according to time series recordings by monitored on-board sensors, or by sophisticated reasoning algorithms that recognize slow changes in behavior as a component degrades over time, fault detection must be performed correctly. Otherwise, failed components may not be replaced as necessary, or one may be unnecessarily serviced due to a false alarm. Both types error interfere with the attainment of AL objectives of minimizing aircraft support and logistics costs. JSF-PHM demands novel algorithmic methods to eliminate false alarms. This is a challenging task, since classification algorithms intended to detect anomalies (unrecognized faults) learn data probability distributions in known fault condition space. Extrapolation outside the training data is problematic; however this is exactly the region where anomalies are expected to occur. Faced with an anomalous condition, proper discrimination between "fault" and "no-fault" will determine the overall false alarm rate of the PHM system. This proposal describes a novel means to achieve a minimal false alarm rate at a given, fixed level of statistical confidence.
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