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Learning envelopes for fault detection and state summarization

This paper discusses a data mining approach for overcoming common problems with the traditional red-line limit-checking approach to fault detection and state summarization. It essentially involves learning and adapting parametric functions which provide context-sensitive bounds on historic time-series engineering data. Such bounds are suitable as dynamic plug-in replacements for static red-line values. They enable significantly earlier detection while maintaining low false alarm rates. An example is discussed from onboard tests of this technology during the NASA Deep Space 1 (DS1) mission.