Value of information analysis in non-stationary stochastic decision environments: A reliability-assisted POMDP approach. (January 2022)

• Record Type: Journal Article
• Authors: Song, Chaolin; Zhang, Chi; Shafieezadeh, Abdollah; Xiao, Rucheng
• Is Part Of: Reliability engineering & system safety. Volume 217(2022)
• Publication Date: 2022-01
• Subjects: Value of information -- Reliability methods -- Partially observable Markov decision processes -- Bayes' theorem -- Non-stationary environments; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2021.108034
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A new approach, VoI-R-POMDP, is proposed for value of information analysis in non-stationary environments. A POMDP model is introduced that reliably describes non-stationary deterioration processes. New methods for accurately establishing the initial belief, and transition and observation functions are developed. A corroding beam problem is analyzed and the performance of VoI-R-POMDP relative to existing models is demonstrated. Abstract: Optimal management of systems over their service life as they face a multitude of uncertainties remains a significant challenge. While additional information can reduce uncertainties, collecting new information incurs cost and may include observation error. Value of Information (VoI) analysis facilitates quantitative assessment of the expected net benefits of collecting new information. Moreover, partially observable Markov decision processes (POMDPs) can be integrated within VoI analysis to efficiently capture the sequential decision-making environments for systems. The assumption of stationary environment in existing POMDP frameworks for VoI analysis may not be valid, however, in many applications such as deterioration processes which are often non-stationary. To address this gap, this paper presents a new approach called VoI-R-POMDP. A new POMDP framework is proposed to accurately describe non-stationary processes using multiple integrated transition models. New strategies based on reliability concepts are developed to accurately and efficiently determine the parameters of the proposed POMDP model based on prior information. A new formulation of the observation function based on Bayes' theorem is also derived. The proposed framework is applied to a corroding beam example. Results indicate that VoI-R-POMDP can accurately and efficiently describe the deterioration process and thus provide accurate VoI estimates for non-stationary systems.