Title: Degradation model selection using a case-based reasoning system

Authors: Maryam Kheirandish; Rassoul Noorossana; Mohamad Reza Nayebpour; Hossein Najmizadehbaghini

Addresses: Center for Data Science, School of Nursing, Emory University, Atlanta, GA, USA ' Information Systems and Operations Management Department, College of Business, University of Central Oklahoma, Edmond, OK, USA ' Marilyn Davies College of Business, University of Houston – Downtown, Houston, TX, USA ' Information Systems and Operations Management Department, College of Business, University of Central Oklahoma, Edmond, OK, USA

Abstract: The use of degradation data for reliability analyses of products has gained popularity over the past few decades. This approach to reliability analysis reduces the time required to assess the reliability of certain products. However, identifying a suitable model that best fits the degradation data is time-consuming and highly dependent on the practitioner's experience and the amount of available information in the knowledge database. In this study, an expert system utilising case-based reasoning was developed to reduce the time required for reliability analysis. This system is built on the R5 model, where the retrieval phase employs the K-nearest neighbour algorithm with fuzzy weights to identify similarities. Several studies were reviewed to extract effective features influencing the form of degradation models. The performance of the retrieval phase was simulated to demonstrate its practical usefulness. An initial case base, consisting of real cases, was created for the initial use of experts.

Keywords: degradation models; case-based reasoning; CBR; K-nearest neighbour; fuzzy weighing; reliability.

DOI: 10.1504/IJQET.2024.147897

International Journal of Quality Engineering and Technology, 2024 Vol.10 No.4, pp.316 - 335

Received: 27 Jul 2024
Accepted: 09 Jan 2025
Published online: 07 Aug 2025 *

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