Degradation Modeling from Condition-based Data to Functional Failure Signature Data

  • James P Hofmeister Ridgetop Group, Inc. 3580 West Ina Road Tucson, AZ 86741
  • Douglas L Goodman Ridgetop Group, Inc. 3580 West Ina Road Tucson, AZ 86741
  • Ferenc Szidarovszky Ridgetop Group, Inc. 3580 West Ina Road Tucson, AZ 86741
Keywords: Condition-based data, degradation signatures, fault-to-failure progression signature, degradation progression signature, functional-failure signature, prediction information, prognostic health management, failure mode

Abstract

This article describes approaches to degradation modelling starting with condition-based data (CBD) and progressing to functional-failure signature (FFS) data: FFS data forms a transfer curve that is very amenable to processing by prediction algorithms in support of Prognostic Health Management/Monitoring (PHM) systems. Failure modes generate characteristic CBD signatures that are correlated to changes in value of a parameter as degradation progresses. Signature features such as amplitude or frequency are extracted from CBD signature and processed by degradation models that transforms curvilinear, CBD signatures into degradation signatures that are less curvilinear, which increases the accuracy of prediction information such as remaining useful life (RUL) and state of health (SoH). The focus of this article is the theory of degradation-signature models and the use of those models to transform CBD signatures into fault-to-failure progression (FFP) signatures, then into degradation-progression signature (DPS) data, and lastly into FFS data.

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Published
2019-03-29
Section
Articles

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