The utilization of steady state monitoring techniques has become an established means of providing diagnostic and prognostic information for systems and equipment. This is mainly driven by both the wealth of available analysis techniques and the comparatively larger amount of data. However, steady state data is not the only, or in some cases, even the best source of information regarding the health and state of a system. Transient data has largely been overlooked as a source of system information due to the additional complexity in analyzing these types of signals. Time Distribution Mapping via the Sharp Transform allows for a fast, intuitive, generic quantification of deviations a transient signal from an established norm. Without regard to the type or source of the signal, referencing to an established Time Distribution Map can implicitly capture shifts mean, standard deviation, skewness, or even gross frequency shifts without need of additional processing. By quantifying and trending these shifts, an accurate measure of system heath can be established and utilized by prognostic algorithms. In fact, for some systems the elevated stress levels during transients can provide better, more clear indications of system health than those derived from steady state monitoring.
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