A Bayesian Model for Monitoring and Generating Alarms for Deteriorating Systems Working Under Varying Operating Conditions

Ramin Moghaddass (), Zachary Bohl (), Raul Billini () and Shihab Asfour ()
Additional contact information
Ramin Moghaddass: University of Miami
Zachary Bohl: University of Miami
Raul Billini: World Fuel Services
Shihab Asfour: University of Miami

A chapter in Multicriteria and Optimization Models for Risk, Reliability, and Maintenance Decision Analysis, 2022, pp 249-281 from Springer

Abstract: Abstract Most mechanical systems work under varying operating conditions and stress levels that can potentially influence how the degradation and damage processes evolve over time. To analyze the progression of damage in such systems over time, this chapter develops a new stochastic model based on a well-known damage model using Bayesian hierarchical structure that takes into account uncertainty and interpretability in a mathematically convenient and structured manner. First, a hierarchical model is defined that can track the damage level of a deteriorating system over time based on the number of cycles spent at each stress level. A parameter estimation model is then defined that can employ past data to train the structure of the model. A method for predicting remaining useful life and the uncertainty associated with it is developed that can dynamically be updated based on the history of the operating conditions. An optimal decision policy is introduced that can determine the optimal time to issue an alarm given an ideal warning time defined by the maintenance decision makers. Finally, the application of the model and its accuracy are demonstrated through simulation experiments and a real-world case study for wind turbine health monitoring.

Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-89647-8_12

Ordering information: This item can be ordered from
http://www.springer.com/9783030896478

DOI: 10.1007/978-3-030-89647-8_12

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().