MULTI LINEAR REGRESSION-BASED IOT AND FOG COMPUTING ON MAINTENANCE PREDICTIONS APPROACH FOR EFFICIENT ASSET MANAGEMENT IN INDUSTRY REVOLUTION 4.0

Singh R.K.; Abdufattokhov S.; Godla S.R.; Rao V.S.; Baker El-Ebiary Y.A.; Borda R.F.C. · Journal of Theoretical and Applied Information Technology (2023)

Resumen

Industry 4.0 makes it possible for new developments in technology like Big Data Analytics and Machine Learning to be successfully incorporated into and combined with current production processes, allowing smart manufacturing. With the use of predictive maintenance, a company owner can make decisions like replacing or fixing a component before it fails and affects the entire production line. To optimize work distributions and maintenance prediction models, Industry 4.0 (I4.0) necessitates good asset management. The Multi Linear Regression (MLR) based predictive maintenance in IoT and fog computing is presented in this study. The data produced by I4.0's Industrial Internet of Things (IIoT) enables information transparency and process management. Regular maintenance enables the business manager to make choices like when to fix or substitute a part before it malfunctions and disrupts the whole manufacturing process. Thus the study illustrates a forecasting model for predicting rapid failure in industrial machinery and to enable an effective production and servicing process. The outcome demonstrates that the suggested solution performs better than current methods in terms of computational cost execution time, and energy usage. In comparison to the second-best outcomes, the execution period is quicker, the cost is less, and the amount of energy consumption is lower.

Instituciones: Universidad Privada del Norte