Development of a Predictive Analytics System for the Technical Condition of the Sinter Extractor Based on Artificial Intelligence Methods

This article describes approaches to building a software system that allows for the prediction of possible failures and malfunctions of industrial equipment based on data on its condition, which will significantly affect the safety of work and the effective functioning of the enterprise. For the task of predicting equipment failures, a model based on soft voting between three algorithms with different approaches to classification is proposed: convolutional neural network, logistic regression, and the support vector method. A model based on an isolating forest algorithm and an LSTM-based neural network is proposed to predict failures. A web service has been developed that implements the main functions of a predictive analytics system based on artificial intelligence methods: monitoring the technical condition of the excavators in real time, statistical analysis of malfunctions, fault prediction, and model training.