Scientists of the Silesian University of Technology (SUT) have been carrying out research into advanced methods of diagnostics of machines, devices and industrial processes for many years. A lot of the solutions developed in the Silesian University of Technology have been successfully implemented in industrial conditions. It is worth mentioning that the new technologies currently presented as part of the Industry 4.0 concept were created much earlier at universities, including the Silesian University of Technology, where they have been developed for years, e.g. methods of automatic inference and prediction of the technical condition of various classes of objects.

One of the key conditions for the successful implementation and realization of the Industry 4.0 concept is the realization of activities focusing on ensuring the exploitation potential of the machinery park at an appropriately high level. These activities involve comprehensive diagnostic supervision and detailed reliability analysis and have a significant impact on the decision-making process resulting from the established maintenance strategy.

Research activity in the Technical diagnostics, machine operation and maintenance systems sub-area within the POB5 Priority Research Area focus on three key aspects.

The first aspect includes modern technical diagnostics. The diagnostic methods developed and implemented in the industry use artificial intelligence and soft computing algorithms. The issues include the analysis of complex chromatographic and spectrometric signals, the analysis of machine vibration signals, the use of active and passive thermovision, image processing and calibration algorithms for measuring instruments and devices with calibration automation. The developed solutions are implemented on AGV and AMR industrial platforms and in systems for testing components at any stage of the production process as well as for final product testing (In-Line and End of Line Testers). An important supplement to the diagnostic tests is the creation and development of innovative solutions in the field of measurement data analysis algorithms and measurement uncertainty estimation.

The second aspect focuses on the issues of effective exploitation of machines and devices. Research in this area concerns the modeling of events and exploitation processes for individual machines as well as entire production lines. The developed solutions are the basis for reliability analyses and, as a result, for the construction and verification of indicator assessment models, taking into account individual diagnostic features of particular machines and devices.

The third aspect relates to maintenance systems. The research interest in this case is in exploitation  decision-making problems, resulting from the need to search for ways to extend the periods of use and shorten the unused periods of machines and devices, while maintaining the assumed level of quality of the works (mainly maintenance works). The research focuses on modeling, assessing and shaping the exploitation policy of enterprises (considered especially in the long-term perspective), based on the optimization of maintenance strategies. A particularly important part of the research is the prescriptive maintenance strategy, whose features are consistent with the assumptions of the Industry 4.0 concept.