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Abstract

This article addresses diagnosis and prognosis of evolving fatigue crack damage in polycrystalline alloy structures. It presents a statistically inspired recursive method for in situ estimation of the remaining useful life in machinery components, based on real-time measurements. The underlying algorithm is built upon (a) symbolic dynamic filtering of (online) ultrasonic sensor data and (b) Karhunen–Loève decomposition of optical measurements for (off-line) construction of a stochastic model of fatigue crack propagation. The proposed method has been experimentally validated on a computer-instrumented and computer-controlled fatigue test apparatus for estimation of crack damage and prediction of the remaining useful life in test specimens, made of 7075-T6 aluminum alloys.

Keywords

fatigue crack propagation remaining useful life estimation symbolic dynamic filtering

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Online recursive estimation of remaining life using ultrasonic measurements

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