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Abstract

In this paper, an optimization approach, which is based on the Bayesian Linear Inference (BLI) model, has been proposed for the maintenance of Programmable Logic Controllers (PLCs). The BLI model, which is implemented using historical data, incorporates maintenance indicators like the number of failures (NF), total downtime (TD), total unexpected intervals (TUI), mean time to repair (MTTR) and mean time between failures (MTBF). It offers a probabilistic framework for determining the influence of each predictor variable on PLC maintenance. The model produces posterior means, credible intervals, and standard deviations, which provide insights into the magnitude and uncertainty of these relationships. The results from the study show that factors like NF and TD are influenced by the magnitude and direction of the maintenance levels. Also, the R-squared score (0.85) also indicates how much of the variability in maintenance in the system. From the results obtained, the study can conclude that the BLI model can optimize PLC maintenance procedures by identifying essential components and their contributions. Also, it is able to estimate future maintenance requirements and helps with resource allocation and process optimization decisions.

Keywords

Programmable Logic Controllers Bayesian Linear Inference Total Downtime Total Unexpected Intervals Mean Time To Repair Mean Time Between Failures

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Obele, A. F., Aikhuele, D. O. ., & H.U, N. . (2024). The application of the Bayesian linear regression model to optimize the maintenance of a programmable logic controller . Future Technology, 3(3), 8–14. Retrieved from https://fupubco.com/futech/article/view/159
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