Development of False Alarm Mitigation Techniques

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$79,999.00
Award Year:
2005
Program:
SBIR
Phase:
Phase I
Contract:
N68335-05-C-0258
Agency Tracking Number:
N051-028-0497
Solicitation Year:
2005
Solicitation Topic Code:
N05-028
Solicitation Number:
2005.1
Small Business Information
INTELLIGENT AUTOMATION CORP.
13029 Danielson Street, Suite 200, Poway, CA, 92064
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
097858836
Principal Investigator:
Joel Bock
Principal Investigator
(858) 679-4140
joel.bock@iac-online.com
Business Contact:
Nicholas Brunski
Contracts Manager
(858) 679-4140
nick.brunski@iac-online.com
Research Institution:
n/a
Abstract
An 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.

* information listed above is at the time of submission.

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