Application of stochastic filtering for lifetime prediction Export

@article{citeulike:1724093, abstract = {This paper introduces a stochastic filtering modeling approach for predicting the remaining lifetime of a component based on information on the stochastic degradation process and uncertain condition monitoring measurements. The model is illustrated by a case study, where the degradation is assumed to be a simplified fatigue crack growth process. The model accounts for uncertainties in both degradation process and condition measurements in a sound way. If completed with information on costs of monitoring, failure and replacement, such model could be used in optimizing both the condition monitoring intervals and, e.g. the replacement time for the component.}, author = {Myotyri, E. and Pulkkinen, U. and Simola, K.}, booktitle = {Selected Papers Presented at QUALITA 2003}, citeulike-article-id = {1724093}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.ress.2005.01.002}, citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6V4T-4FPJ9X5-1/2/5f9458cf971f33c1e071dc26fbe000d9}, doi = {10.1016/j.ress.2005.01.002}, journal = {Reliability Engineering \& System Safety}, keywords = {lifetime-prediction}, month = {February}, number = {2}, pages = {200--208}, posted-at = {2007-10-03 14:53:59}, priority = {2}, title = {Application of stochastic filtering for lifetime prediction}, url = {http://dx.doi.org/10.1016/j.ress.2005.01.002}, volume = {91}, year = {2006} }

TY - JOUR ID - citeulike:1724093 L3 - citeulike-article-id:1724093 N2 - This paper introduces a stochastic filtering modeling approach for predicting the remaining lifetime of a component based on information on the stochastic degradation process and uncertain condition monitoring measurements. The model is illustrated by a case study, where the degradation is assumed to be a simplified fatigue crack growth process. The model accounts for uncertainties in both degradation process and condition measurements in a sound way. If completed with information on costs of monitoring, failure and replacement, such model could be used in optimizing both the condition monitoring intervals and, e.g. the replacement time for the component. IS - 2 JF - Reliability Engineering & System Safety EP - 208 TI - Application of stochastic filtering for lifetime prediction VL - 91 T2 - Selected Papers Presented at QUALITA 2003 SP - 200 KW - lifetime-prediction AU - Myotyri, E AU - Pulkkinen, U AU - Simola, K PY - 2006/02// UR - http://dx.doi.org/10.1016/j.ress.2005.01.002 ER -