The Industry 4.0 Concept assumes that the majority of industry’s resources will be able

to self-diagnose; this will, therefore, enable predictive maintenance. Numerically controlled

machines and devices involved in technological processes should, especially, have the facility

to predict breakdown. In the paper, the concept of a predictive maintenance system for

a vacuum furnace is presented. The predictive maintenance system is based on analysis of the

operating parameters of the system and on the algorithms for identifying emergency states in

the furnace. The algorithms will be implemented in the monitoring sub-system of the furnace.

Analysis of the operating parameters of vacuum furnaces, recorded in the Cloud will lead to

increased reliability and reduced service costs. In the paper, the research methodology for

identification of the critical parameters of the predictive maintenance system is proposed.

Illustrated examples of the thermographic investigation of a vacuum furnace are given.

Predictive maintenance is one of the key aspects of Industry 4.0. The article presents the results of experimental tests of nitrogen purification filters in the installation of a low-pressure, metal processing device. The aim of the research was to develop a predictive algorithm for making decisions regarding the replacement of used filters, based on flow analysis and measurement of the pressure difference in front of and behind the tested filter. For the purposes of the research, a special test stand was constructed, which made it possible to determine the operating characteristics of three selected filters. Based on the tests carried out, the limit characteristics of the parameters measured were determined, identifying the need to replace filters in the gas installation.