An approach for assessment of level of degradation and RUL estimation for a dynamic systeme
| Autor: | Manuel Avila, Frédéric Kratz, Toufik Aggab, Pascal Vrignat |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
021103 operations research
Observer (quantum physics) Computer science 020208 electrical & electronic engineering 0211 other engineering and technologies Process (computing) 02 engineering and technology Residual Control theory 0202 electrical engineering electronic engineering information engineering Prognostics Cluster analysis Hidden Markov model Induction motor Degradation (telecommunications) |
| Zdroj: | IECON |
| DOI: | 10.1109/iecon.2017.8216534 |
| Popis: | In this paper, we propose an approach for failure prognosis by assessing the level of degradation and estimating the residual life before failure of a dynamic system. During its operation, an industrial process as a dynamic system evolves physically. This evolution can have an impact on the modes of operation: nominal, degraded or failure. The proposed approach is based on knowledge of the behavior model of the system. It deals with systems where available measurements are not able to provide information on the level of degradation reached by the system. Since the available data are not usable, and in order to generate indicator signals of degradation, the method based on the generation of residues by an observer was selected. Due to the model uncertainties and disturbances, the evolution of the characteristics of residues is not necessarily correlated with the evolution of the level of degradation. Therefore, the residues generated were exploited to model the degradation behavior. We selected a Hidden Markov Model or its multi-stream extension in the case of a stream of residuals. The proposed approach was conducted in two phases: a learning phase to model the degradation behavior and a prognosis phase to assess the level of degradation and estimate the Remaining Useful Life. To illustrate the approach, a simulated induction motor was used. |
| Databáze: | OpenAIRE |
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