Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Integrated Fuzzy Fault Tree Model With Bayesian Network-based Maintenance Optimization of Complex Equipment in Automotive Manufacturing

Version 1 : Received: 23 August 2021 / Approved: 26 August 2021 / Online: 26 August 2021 (09:49:42 CEST)

How to cite: Soltanali, H.; Khojastehpour, M.; Farinha, J.T. An Integrated Fuzzy Fault Tree Model With Bayesian Network-based Maintenance Optimization of Complex Equipment in Automotive Manufacturing. Preprints 2021, 2021080501 (doi: 10.20944/preprints202108.0501.v1). Soltanali, H.; Khojastehpour, M.; Farinha, J.T. An Integrated Fuzzy Fault Tree Model With Bayesian Network-based Maintenance Optimization of Complex Equipment in Automotive Manufacturing. Preprints 2021, 2021080501 (doi: 10.20944/preprints202108.0501.v1).

Abstract

Knowledge-based approaches are useful alternatives to predict the Failure Probability (FP) coping with the insufficient data, process integrity, and complexity issue in manufacturing systems. This study proposes a Fault Tree Analysis (FTA) approach as proactive knowledge-based technique to estimate the FP based maintenance planning with subjective information from domain experts. However, the classical-FTA still suffers from uncertainty and static structure limitations which poses a substantial dilemma in predicting FP. To deal with the uncertainty issues, a Fuzzy-FTA (FFTA) model was developed by statistical analysing the effective attributes such as experts' trait impacts, scales variation and, assorted membership and defuzzification functions. Besides, a Bayesian Network (BN) theory was conducted to overcome the static limitation of classical-FTA. The results of FFTA model revealed that the changes in decision attributes were not statistically significant on FP variation while BN model considering conditional rules to reflect the dynamic relationship between events had more impact on predicting FP. After all, the integrated FFTA-BN model was used in the optimization model to find the optimal maintenance intervals according to estimated FP and the total expected cost. As a practical example, the proposed model was implemented in a semi-automatic filling system in an automotive production line. The outcomes could be useful for upgrading the availability and safety of complex equipment in manufacturing systems.

Keywords

Automotive industry; Bayesian network; Fault tree analysis; Fuzzy set theory; Maintenance optimization; Uncertainty

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.