Intelligent Predictive Maintenance Strategy for Hybrid Systems Using Model-Data Fusion Approach

Chenyu Xiao, Pai Zheng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

This paper proposes an intelligent predictive maintenance framework subjected to multiple faults in hybrid systems. This framework embeds a model-data fusion approach, where the reinforcement extended Kalman filter (REKF) is for model-based fault estimation and the biogeography based optimization-support vector regression (BBO-SVR) is for data-driven remaining useful life (RUL) prediction. The intelligent predictive maintenance framework for hybrid systems can be constructed as follows. Firstly, the REKF is adopted for fault estimation based on state space equations of the hybrid system to identify the degradation processes of faulty components. Then, an event-driven decision module is designed to sequentially judge the states of RUL prediction procedure and maintenance procedure. After that, the BBO-SVR is developed to predict the RULs of faulty components under different health monitoring stages of the hybrid systems, which are determined by the results of event-driven decision module. Finally, the feasibility of the above proposed intelligent predictive maintenance framework will be validated by a case study.

Original languageEnglish
Title of host publication2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9798350320695
DOIs
Publication statusPublished - Aug 2023
Event19th IEEE International Conference on Automation Science and Engineering, CASE 2023 - Auckland, New Zealand
Duration: 26 Aug 202330 Aug 2023

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2023-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference19th IEEE International Conference on Automation Science and Engineering, CASE 2023
Country/TerritoryNew Zealand
CityAuckland
Period26/08/2330/08/23

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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