Machine Remaining Useful Life Prediction Based on Adaptive Neuro-Fuzzy and High-Order Particle Filtering

Chaochao Chen, George Vachtsevanos, and Marcos E. Orchard
Submission Type: 
Full Paper
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phmc_10_082.pdf221.7 KBOctober 17, 2010 - 6:40pm

Machine remaining useful life (RUL) prediction is a key part of Condition-Based Maintenance (CBM), which provides the time evolution of the fault indicator so that maintenance can be performed to avoid catastrophic failures. This paper proposes a new RUL prediction method based on adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which predicts the time evolution of the fault indicator and computes the probability density function (pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter to describe the fault propagation process; the high-order particle filter uses real-time data to update the current state estimates so as to improve the prediction accuracy. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results show that it outperforms the conventional ANFIS predictor.

Publication Control Number: 
082
Submission Keywords: 
Fatigue Prognosis; Adaptive Neuro-Fuzzy; High-Order Particle Filtering; Bayesian Estimation
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