Model-Based Prognostics Under Non-stationary Operating Conditions



Published Sep 25, 2011
Matej Gašperin Pavle Boškoski Dani Juričić


The paper presents a novel approach for prognostics of faults in mechanical drives under non-stationary operating conditions. The feature time series is modeled as an output of a dynamical state-space model, where operating conditions are treated as known model inputs. An algorithm for on-line model estimation is adopted to find the optimal model at the current state of failure. This model is then used to determine the presence of the fault and predict the future behavior and remaining useful life of the system. The approach is validated using the experimental data on a single stage gearbox.

How to Cite

Gašperin, . M. ., Boškoski, P. ., & Juričić, D. . (2011). Model-Based Prognostics Under Non-stationary Operating Conditions. Annual Conference of the PHM Society, 3(1).
Abstract 166 | PDF Downloads 95



model-based prognostics, dynamic linear model, expectation-maximization algorithm, non-stationary operating conditions

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