SBIR-STTR Award

Communications systems depend on the health of their equipment and components to perform reliably over extended periods of time. Maintaining the health of equipment in remote, unmanned communications terminals is especially critical, because unscheduled repairs take longer and have more impact on system availability than is the case for manned facilities with short logistics lines. Instrumental Sciences, Inc. (ISI) proposes new predictive fault detection methods, combining a model-based architecture with multivariate signal processing, trend analysis, identification techniques, and situation assessment algorithms. The proposed approach integrates the techniques into a cognitive processing architecture with decision algorithms that provides timely maintenance action recommendations for maintainers to perform before actual failures occur. The predictive fault detection capability enables predictive condition-based maintenance, which minimizes downtime and use resources efficiently, to replace schedule-based preventive maintenance, which is costly and impacts readiness. Primary objectives of the proposed Phase I effort are to (1) demonstrate the feasibility of the proposed predictive fault detection approach for unmanned communications facilities and (2) evaluate the potential system availability improvements and life cycle cost savings of this predictive fault detection capability over conventional schedule based maintenance and other prognostics methods

Communications systems depend on the health of their equipment and components to perform reliably over extended periods of time. Maintaining the health of equipment in remote, unmanned communications terminals is critical, because unscheduled repairs have significant impact on system availability. In Phase I of this SBIR project Instrumental Sciences, Inc. (ISI) researched methods to improve the effectiveness of communications systems/facilities maintenance through development of new predictive fault detection (PFD) methods and architectures, employing multivariate statistical analysis, trending analysis, and Bayes cost decision algorithms to produce an innovative statistical analysis engine capable of processing a variety of input signal types. In the Phase II continuation, ISI will develop an engineering prototype PFD system, building on the successful Phase I results, integrate it with an IFICS Data Terminal (IDT) testbed, and conduct tests in the IDT environment. The ISI approach combines statistical signal processing, trend analysis, identification, and decision-theoretic techniques with model-based representations of measurable equipment properties that can yield clues concerning the condition of the covered equipment.

Keywords:
Fault Detection, Prognostics, Condition-Based Maintenance, Diagnostics, Data Acquisition, Statistics, Trend Analysis