N141-054 TITLE: Predictive Condition-Based Maintenance for High-Powered Phased Array Radar Systems

TECHNOLOGY AREAS: Sensors, Electronics, Battlespace

ACQUISITION PROGRAM: PEO IWS 1.0, Integrated Combat Systems, AEGIS

OBJECTIVE: Develop an innovative, predictive condition-based radar maintenance forecaster for high-powered radar systems to reduce maintenance costs and increase operational availability.

DESCRIPTION: An innovative predictive algorithm for forecasting the performance and maintenance of naval radar systems is required to address issues with the present method of maintenance. Currently, preventative maintenance for high-powered phased-array multi-function naval radars is primarily time-based and dependent upon human monitoring of subject systems. Computer-controlled test and monitor systems provide system status and allow for monitoring of key sub-system parameters such as voltage, power, capacitance, etc., but this data is not captured and thus not analyzed over time. Preventative maintenance is not driven by automated system status or performance indicators and trends. Thus, maintenance is performed inefficiently and often fails to predict or prevent component and system failures.

Corrective radar system maintenance usually occurs after a component or system fails, or if component degradation is observed during routine preventative maintenance. Failure to anticipate corrective maintenance requirements increases mean time to repair (MTTR), and decreases operational availability (Ao). Unanticipated corrective maintenance actions also drive up costs due to increased labor costs and expedited shipping costs when parts have to be obtained quickly.

Expert systems have been developed over the past 40 years that are used in a number of fields where diagnoses or predictions are possible and useful. As a subset of the computer application known as artificial intelligence, an expert system is capable of making decisions and can be trained to improve its performance [Ref 1].

An expert system that can use readily available, but not currently recorded, radar system performance parameters to predict and thus preempt component and system failures is sought to improve overall system Ao, reduce MTTR, and reduce system maintenance and repair costs [Ref 2].

An example of a desired solution is an innovative expert system that continuously monitors the radar�s component parametric data streams and then conducts trend analysis. The expert system would combine the trend analysis data with component degradation and failure data reports to improve its prediction algorithms. The desired result is a system that is capable of providing a report such as, "switch tube "A" has a 90% probability of failure within the next 72-96 operating hours" or "the output of component "B" decreased by 10% in the last 7 days with the rate of output decrease accelerating significantly in the last 24 operating hours, indicating there is an 89% probability of component failure in the next 96 operating hours."

The Navy seeks an innovative condition-based maintenance technology (i.e., a predictive condition-based radar maintenance forecaster) that can use adaptive learning techniques to "understand" component interdependencies and can accurately predict component failure of radar transmitters based on all available parametric data. The specific parts of interest include, but are not limited to, all vacuum and traveling wave tubes (TWT), high voltage power supplies, inverters, sidewall capacitors, faint rectifiers, and cross-field amplifiers. The technology will correlate to the component parametric data and potentially the modes of operation of the radar, power output, phase waveforms, waveform demands, waveguide VSWR, voltages, and will adaptively "learn" to associate different conditions to different failure rates. [Ref 3]

PHASE I: The company will develop a concept for a predictive condition-based radar maintenance forecaster that meets the requirements described above. The company will demonstrate the feasibility of the concept in meeting Navy needs and will establish that the concept can be feasibly developed into a useful product for the Navy. Feasibility will be established by testing and analytical modeling. The small business will provide a Phase II development plan that addresses technical risk reduction and provides performance goals and key technical milestones.

PHASE II: Based on the results of Phase I and the Phase II development plan, the small business will develop a prototype for evaluation. The prototype will be evaluated to determine its capability in meeting the performance goals defined in the Phase II development plan and the Navy requirements for the predictive condition-based radar maintenance forecaster. System performance will be demonstrated through prototype evaluation and modeling or analytical methods over the required range of parameters including numerous deployment cycles. Evaluation results will be used to refine the prototype into an initial design that will meet Navy requirements. The company will prepare a Phase III development plan to transition the technology to Navy use.

PHASE III: The company will be expected to support the Navy in transitioning the technology for Navy use. The company will develop the predictive condition-based radar maintenance forecaster according to the Phase III development plan for evaluation to determine its effectiveness in an operationally relevant environment. The company will support the Navy for test and validation to certify and qualify the system for Navy use.

PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: Numerous commercial air-search radars are in use today. A predictive maintenance forecaster would improve the operational reliability of these radars and improve their availability. Commercial radar manufacturers would be able to incorporate the technology into their radar condition monitoring software. This is an innovative capability that can be used in a growing and wide spectrum of sensors, electronic and high powered radar systems to increase operational availability (Ao).

REFERENCES:
1. Giarratano, Joseph C. and Riley, Gary. Expert Systems, 3rd Edition. Boston, MA: PWS Publishing Co., 1998.

2. Williams, John H., Davies, Alan, and Drake, Paul R. Condition-based Maintenance and Machine Diagnostics. London: Chapman & Hall, 1994.

3. Mailloux, R.J., Phased Array Antenna Handbook. Norwood, MA: Artech House, 2005.

KEYWORDS: Expert Systems; Phased Array Radar; Condition-Based Maintenance; Adaptive Learning; Mean Time to Repair (MTTR); Prediction Algorithms

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