Recent research on mechanical diagnostics technology has revealed the possibility of utilizing advanced mechanical diagnostics technology capabilities in real-time environments. The goal of the current investigation was to conduct systematic interview's with flight crew regarding their information requirements for using new diagnostic systems to predict and mitigate inflight mechanical system emergencies. Future research in this area will determine how best to present information to the aircrew in order to improve aircrew safety and enhance mission effectiveness.
The machinery condition monitoring community continues its search for the ‘‘Holy Grail’’ of prognostics. The problem is that there is no common agreement on two questions: ‘‘What does the term prognostics mean in this context’’? and ‘‘What must be done to achieve it’’? This paper differentiates ‘‘predictive maintenance’’ (in its generally accepted form) from ‘‘condition-based maintenance’’ (which requires prognostics). A justification for development and implementation of the more advanced and cost-effective condition-based maintenance is presented. A working definition of prognostics for the condition-based maintenance community is proposed. Further, a methodology for setting acceptable false alarm rates for both diagnostics and prognostics is presented to frame the problem for the research community in terms of the application environment, particularly for mission critical equipment. [See NOISE-CON Proceedings for full paper.]
In a previous paper (Hansen et al., 1995), a conceptual framework for developing a true prognostic or predictive diagnostic capability was described. The current paper expands on this framework by describing micro-mechanical and dynamic models, sensors and data fusion, signal processing, approximate reasoning, distributed architecture, and human factors research and development being conducted to provide such a capability for a broad range of applications. These include both autonomous and man-in-the-loop decision making about maintenance actions and local and geographically distributed monitoring and data analysis architectures.
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