Title :
Probabilistic model based algorithms for prognostics
Author :
He, David ; Wu, Shenliang ; Banerjee, Pat ; Bechhoefer, Eric
Author_Institution :
Dept. of Mech. & Ind. Eng., Illinois Univ., Chicago, IL
Abstract :
In this paper, two prognostics algorithms to accurately predict the state of a complex system as a function of time are presented. The algorithms are developed based on a hidden semi-Markov model (HSMM) and validated on a real-world helicopter rotor track and balance prognosis problem. It is shown that the developed prognostic algorithms provide a good performance in predicting the time to the next required rotor track and balance action for two different application scenarios
Keywords :
aircraft maintenance; helicopters; hidden Markov models; rotors; balance prognosis problem; helicopter rotor track; hidden semi-Markov model; probabilistic model; prognostics algorithms; Discrete wavelet transforms; Helicopters; Helium; Industrial engineering; Life estimation; Machinery; Neural networks; Predictive models; Recursive estimation; Vibrations;
Conference_Titel :
Aerospace Conference, 2006 IEEE
Conference_Location :
Big Sky, MT
Print_ISBN :
0-7803-9545-X
DOI :
10.1109/AERO.2006.1656122
