Despite significant attention to online health monitoring and prognostics of bearings, many common health indicators are not sensitive to early stages of degradation. This research investigates the use of approximate entropy (ApEn), previously developed for fault diagnostics, as a health indicator for prognostics. ApEn quantifies the regularity of a signal; as bearings degrade, the frequency content of vibration signals changes and affects the ApEn as the vibration becomes more chaotic. Early results suggest ApEn supports earlier degradation detection and more predictable progression from fault to failure. This research focuses on optimizing parameters of the ApEn calculation to provide guidance across a variety of bearing types, sizes, and geometries in both steady-state and transient operation.
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