An Intelligent Monitoring System for Execution of Machine Engineering Processes

Abstract

An integral part of the production system is monitoring technological process. This paper proposes assessment procedures for the characteristics of process operations, making it possible to track both the failure rate of each machine and the entire system at each stage of the technological process. A process simulation model is built, describing technological processes of various durations and sets of operations, which is based on Markov modeling, when the number of different system states is finite, and variations in the system states occur at different discrete instants of time. The initial formation of a set of contingency factors in the production system is based on the Delphi approach. Using the Pareto principle makes it possible to identify the most common factors of process failures. The neural network makes it possible to simulate various situations and evaluate the probability of process violation. The procedure for the formation of a matrix of transition probabilities is proposed for both the initial assessment of the process feasibility and matrices varying discretely in time, depending on the process operation state. Based on the proposed methods, an intelligent monitoring system for machine engineering process execution is developed.