Prognosis of uncertain linear time-invariant discrete systems using unknown input interval observer

Abstract : In this paper, a model-based prognosis method where the degradation cannot be directly measured but only detectable through the drift of a model parameter is considered. This parameter drift is viewed as an unknown input, whose reconstruction allows the estimation of the degradation state. Model-based prognosis is divided into a filtering step where the current degradation state is estimated, and an uncertainty propagation step where the future degradation state is predicted. During these two steps, model uncertainty and measurement uncertainty are taken into account within the set-membership framework using interval techniques. The filtering step is performed with an interval unknown input observer for linear time-invariant discrete systems. Then, based on a set-membership constraint satisfaction methodology , interval propagation is performed to estimate the bounds that include the future degradation state until some failure threshold is reached, allowing the deduction of the interval including the remaining useful life. In order to demonstrate the efficiency of the proposed model-based prognosis methodology, the degradation of a suspension system is studied.
Document type :
Journal articles
Complete list of metadatas

Cited literature [39 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02294534
Contributor : Véronique Soullier <>
Submitted on : Wednesday, September 25, 2019 - 10:27:28 AM
Last modification on : Monday, February 10, 2020 - 6:14:15 PM

Identifiers

Citation

Elinirina Robinson, Julien Marzat, Tarek Raïssi. Prognosis of uncertain linear time-invariant discrete systems using unknown input interval observer. International Journal of Control, Taylor & Francis, 2019, ⟨10.1080/00207179.2019.1648854⟩. ⟨hal-02294534⟩

Share

Metrics

Record views

30