Transforming condition-based data signatures into functional failure signatures

This paper describes the transformation of conditioned-based data (CBD) signatures into functional-failure signatures (FFS) that are particularly amenable to processing by prediction algorithms. CBD signatures comprise feature data (FD) that creates a signature that is highly correlated to degradation. Degradation proceeds from the onset of damage to a level of damage at which a component, and its assembly, no longer functions within operational specifications: functional failure occurs. A CBD-based feature signature correlates a change in value (
dP
) of a parameter of interest (
P
₀
) as degradation progresses. This paper presents a theory that each failure mode generates a characteristic degradation signature:
g
(
dP
,
P
₀
). Further, a feature signature can be transformed into a dimensionless ratio to create a fault-to-failure progression (FFP) signature that is a function of a characteristic degradation signature. Finally, an FFP can be solved in terms of a ratio of parameter change to a parameter to produce degradation progression signature (DPS). Absent noise, a DPS is a linear straight-line transfer curve that is easily transformed into a functional-failure signature (FFS) that is particularly amenable to processing to produce prognostic information in support of Prognosis for Health Monitoring/Management (PHM): (1) an FFS approaches an ideal straight-line transfer curve as noise is ameliorated and/or mitigated; (2) has negative values in the absence of degradation; (3) has positive values below 100 when there is degradation below a defined level of functional failure; and (4) has values at or above 100 when the level of degradation is at or above a level defined as functional failure. Even in the presence of noise and feedback effects, and even when the rate of degradation is nonlinear, a DPS is still a very linear transfer curve. The authors present seven different families of increasing signatures and decreasing signatures that can be represented by seven degradation-signature models that, coupled with models for defining a level of function failure, are used to transform CBD-based signature data into FFS data.