Design of a Fault Tolerance System for Industrial Controllers Utilizing Fractional Ensemble Learning

Abstract

Smart manufacturing systems integrate technological platforms like cloud computing and computational paradigms to facilitate maintenance, monitoring, and automation. Operations and automation control based on devices or controllers depend on their connectivity and the quantity of machines they manage. This leads to further issues of task failures caused by controller interruptions. This article introduces a Fault Tolerance Computable (FTC) Fractional Learning (FL) to address this issue. The suggested computational approach utilizes ensemble learning through two fractional ensemble techniques: stacking and boosting. During the stacking process, the ratio of the preceding maximum fault tolerance rate is equated with the current failure rate. This equalization is optimized from median tolerance to a high rate via recurrent training. The training commences in the stacking state utilizing the previously provided controller logs. The boosting state offers an alternative for the existing job allocations and machine schedules derived from the equalization factor acquired in the preceding state. The controller's output for machine control facilitates swapping at different time intervals. The process is initiated between two consecutive operational states of the industrial controller to optimize task completion rates. Findings: FTC-FL improves task allocation, completion, and tolerance rate by 10.79%, 11.88%, and 9.86% respectively with 11.9% fewer failures.