Evaluating and Optimizing Analytic Signals



Published Nov 24, 2021
Shashvat Prakash Antoni Brzoska


Condition-based maintenance is becoming a viable option for mitigating the high cost of unscheduled repairs. However, as data-driven approaches gain favor, there is a need to preserve the underlying physical degradation models in order to reasonably justify preventative maintenance. One solution is a class of models which augment physics with data-driven heuristics. The nature of the underlying degradation is explained with physics while detectability and decision nuances can be overcome with statistics and signal processing.

This paper describes a process for evaluating analytical models and using this evaluation for improving overall detection. The method involves optimizing a tunable filter to process signals such that the precursor signature preceding failure events approximates a known degradation behavior.

How to Cite

Prakash, S., & Brzoska, A. (2021). Evaluating and Optimizing Analytic Signals. Annual Conference of the PHM Society, 13(1). https://doi.org/10.36001/phmconf.2021.v13i1.2968
Abstract 2231 | PDF Downloads 973



Evaluation, Signal Processing, Classifier

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