FAILURE PROGNOSTICS BY A DATA-DRIVEN SIMILARITY-BASED APPROACH

This paper presents a data-driven, similarity-based approach for prognostics of industrial and structural components. The potentiality of the approach is demonstrated on a problem of crack propagation, taken from literature. The crack growth process is described by a nonlinear model affected by nonadditive noises. A comparison is provided with an existing Monte Carlo-based estimation method, known as particle filtering.

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