Degradation modeling-based remaining useful life estimation¶
Zhang, Z., Si, X., Hu, C., & Kong, X. (2015). Degradation modeling-based remaining useful life estimation: A review on approaches for systems with heterogeneity. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 229(4), 343-355.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Gradual Deterioration
- Modern prognostics integrate these models with sensor data to predict remaining useful life (RUL): knowing the current state (crack size, capacitor capacitance, bearing wear), the degradation model, and the failure threshold, calculate how much time remains before failure
This sourceReviews the mathematical framework of degradation modeling and remaining-useful-life prognostics (stochastic-process and physics-based degradation models, failure-threshold crossing, unit-to-unit and operating-condition variability); supports the claim that degradation follows predictable laws enabling RUL prediction, and that under-margined designs fail prematurely.
Supported in partVerified against the publisher's abstract
The review's abstract confirms degradation modeling as the basis of remaining-useful-life estimation, but says nothing about sensor-data integration or the crack/capacitance/bearing state inputs the claim names.
“This article provides a review on approaches for degradation modeling and remaining useful life estimation, with an emphasis on the heterogeneity in the systems.”
- Modern prognostics integrate these models with sensor data to predict remaining useful life (RUL): knowing the current state (crack size, capacitor capacitance, bearing wear), the degradation model, and the failure threshold, calculate how much time remains before failure
Verification¶
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Registry ID ref:5318e51f052a · see in the full table