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
Evaluation, Signal Processing, Classifier
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