TY - JOUR
T1 - Multiscale modeling of wear degradation in cylinder liners
AU - Moraes, Alvaro
AU - Ruggeri, Fabrizio
AU - Tempone, Raúl
AU - Vilanova, Pedro
PY - 2014
Y1 - 2014
N2 - Every mechanical system is naturally subjected to some kind of wear process that, at some point, will cause failure in the system if no monitoring or treatment process is applied. Since failures often lead to high economical costs, it is essential both to predict and to avoid them. To achieve this, a monitoring system of the wear level should be implemented to decrease the risk of failure. In this work, we take a first step into the development of a multiscale indirect inference methodology for state-dependent Markovian pure jump processes. This allows us to model the evolution of the wear level and to identify when the system reaches some critical level that triggers a maintenance response. Since the likelihood function of a discretely observed pure jump process does not have an expression that is simple enough for standard nonsampling optimization methods, we approximate this likelihood by expressions from upscaled models of the data. We use the Master Equation (ME) to assess the goodness-of-fit and to compute the distribution of the hitting time to the critical level.
AB - Every mechanical system is naturally subjected to some kind of wear process that, at some point, will cause failure in the system if no monitoring or treatment process is applied. Since failures often lead to high economical costs, it is essential both to predict and to avoid them. To achieve this, a monitoring system of the wear level should be implemented to decrease the risk of failure. In this work, we take a first step into the development of a multiscale indirect inference methodology for state-dependent Markovian pure jump processes. This allows us to model the evolution of the wear level and to identify when the system reaches some critical level that triggers a maintenance response. Since the likelihood function of a discretely observed pure jump process does not have an expression that is simple enough for standard nonsampling optimization methods, we approximate this likelihood by expressions from upscaled models of the data. We use the Master Equation (ME) to assess the goodness-of-fit and to compute the distribution of the hitting time to the critical level.
KW - Indirect inference
KW - Master Equation
KW - Multiscale approximation
KW - Pure jump processes
KW - Wear processes
UR - http://www.scopus.com/inward/record.url?scp=84897567744&partnerID=8YFLogxK
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U2 - 10.1137/130927024
DO - 10.1137/130927024
M3 - Article
AN - SCOPUS:84897567744
SN - 1540-3459
VL - 12
SP - 396
EP - 409
JO - Multiscale Modeling and Simulation
JF - Multiscale Modeling and Simulation
IS - 1
ER -