Equipment Condition Modeling Based on Random Forest and Arima Machine Learning Algorithm Stacking

The article discusses algorithms for constructing predicative models of the industrial equipment condition using data analysis and machine learning. The model is based on Random Forest (RF) and ARIMA (AR) algorithms. The authors consider approaches to learning algorithms and optimizing parameters. A block diagram of a time series predictive model applying stacking is presented, as well as an assessment of the simulation results.