Remaining Useful Life Estimation of Critical Components With Application to Bearings
Top Cited Papers
- 26 April 2012
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Reliability
- Vol. 61 (2), 292-302
- https://doi.org/10.1109/tr.2012.2194175
Abstract
Prognostics activity deals with the estimation of the Remaining Useful Life (RUL) of physical systems based on their current health state and their future operating conditions. RUL estimation can be done by using two main approaches, namely model-based and data-driven approaches. The first approach is based on the utilization of physics of failure models of the degradation, while the second approach is based on the transformation of the data provided by the sensors into models that represent the behavior of the degradation. This paper deals with a data-driven prognostics method, where the RUL of the physical system is assessed depending on its critical component. Once the critical component is identified, and the appropriate sensors installed, the data provided by these sensors are exploited to model the degradation's behavior. For this purpose, Mixture of Gaussians Hidden Markov Models (MoG-HMMs), represented by Dynamic Bayesian Networks (DBNs), are used as a modeling tool. MoG-HMMs allow us to represent the evolution of the component's health condition by hidden states by using temporal or frequency features extracted from the raw signals provided by the sensors. The prognostics process is then done in two phases: a learning phase to generate the behavior model, and an exploitation phase to estimate the current health state and calculate the RUL. Furthermore, the performance of the proposed method is verified by implementing prognostics performance metrics, such as accuracy, precision, and prediction horizon. Finally, the proposed method is applied to real data corresponding to the accelerated life of bearings, and experimental results are discussed.Keywords
This publication has 21 references indexed in Scilit:
- A mixture of Gaussians Hidden Markov Model for failure diagnostic and prognosticPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- An integrated structural framework to cost-based FMECA: The priority-cost FMECAReliability Engineering & System Safety, 2009
- A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodologyMechanical Systems and Signal Processing, 2007
- Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: A method for bearing prognosticsJournal of Sound and Vibration, 2007
- Bearing fault detection using wavelet packet transform of induction motor stator currentTribology International, 2007
- Advanced Diagnostic and Prognostic Techniques for Rolling Element BearingsPublished by Springer Science and Business Media LLC ,2006
- A Dynamical Systems Approach to Failure PrognosisJournal of Vibration and Acoustics, 2004
- A tutorial on hidden Markov models and selected applications in speech recognitionProceedings of the IEEE, 1989
- Error bounds for convolutional codes and an asymptotically optimum decoding algorithmIEEE Transactions on Information Theory, 1967
- An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecologyBulletin of the American Mathematical Society, 1967