This paper presents the implementation of a new adaptation algorithm to model the crack propagation of roller bearings and to predict their Remaining Useful Life (RUL). The developed algorithm is designed based on the adaptive auto-step reinforcement-learning method combined with a crack propagation model. The advantage of this algorithm is that it is now able to not only estimate the defect growth rate online, but also to predict the RUL of a roller bearing element. The presented defect propagation model incorporated in this work is an extension to the Paris’s formula that is well known in the fracture mechanics community. Further, a new adaptive filtering technique, referred to as the auto-step, is presented in this paper and is used to estimate the parameters of the crack propagation model in real-time. The prognosis structure is first compares values of both the predicted and the measured defect sizes, and then, tunes the parameters of the crack propagation model. Simulation results obtained by the auto-step method are then compared with results obtained by the Recursive Least Square (RLS) adaptive filter. The proposed prognosis strategy is distinct itself from other approaches in terms of obtaining higher accuracy as well as faster convergence rate.
- Dynamic Systems and Control Division
Fault Prognosis of Roller Bearings Using the Adaptive Auto-Step Reinforcement Learning Technique
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Afshari, HH, Al-Ani, D, & Habibi, S. "Fault Prognosis of Roller Bearings Using the Adaptive Auto-Step Reinforcement Learning Technique." Proceedings of the ASME 2014 Dynamic Systems and Control Conference. Volume 1: Active Control of Aerospace Structure; Motion Control; Aerospace Control; Assistive Robotic Systems; Bio-Inspired Systems; Biomedical/Bioengineering Applications; Building Energy Systems; Condition Based Monitoring; Control Design for Drilling Automation; Control of Ground Vehicles, Manipulators, Mechatronic Systems; Controls for Manufacturing; Distributed Control; Dynamic Modeling for Vehicle Systems; Dynamics and Control of Mobile and Locomotion Robots; Electrochemical Energy Systems. San Antonio, Texas, USA. October 22–24, 2014. V001T08A001. ASME. https://doi.org/10.1115/DSCC2014-5928
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