Czech

doc. Ing. Róbert Jankových, CSc.

Institute of Production Machines, Systems and Robotics

The knowledge of mathematics and physics at the level of graduated technical studies is assumed. Basic knowledge of quality management is also welcomed.

For the exam students will study the prescribed issues. They will also elaborate in writing a specific topic from the maintenance area whose defense will be part of the oral exam. Oral examination is held after the defense of a written topic from the area of maintenance in the form of a discussion on the problems according to the course's curriculum.
The course is checked according to the student´s participation in pre-arranged tutorials.

The objective of the course is to gain knowledge in the area of machine and equipment maintenance.
In the sense of the below mentioned annotation and curriculum, the student will gain a comprehensive knowledge of the area of machine and equipment maintenance including other machine services, which will be useful for his engagement into solving different issues in industrial machinery and electrotechnical practice related to this area.

Programme D-KPI-P: Design and Process Engineering, Doctoral, recommended course

Programme D-KPI-K: Design and Process Engineering, Doctoral, recommended course

20 hours, optionally

1. Machine and equipment operation. Life phases. Maintenance. Failures. Causes, origin and development of failures. Failure mechanism and their external symptoms. Failure classification according to the nature of their origin. Types of machinery wearing.
2. Machine and equipment conditions. Technical diagnostics – diagnosis, prognosis, genesis. Diagnostic tools. Off-line (walking) and on-line diagnostics, monitoring, condition monitoring, diagnostic models, diagnostic systems, diagnostic signals – analyses and processing.
3. Sensors, sensor parameters, sensors of mechanical quantities . Sensors of mechanical vibration, sensors of mechanical tension (tensometers), pressure gauges, fluid flowmeters, level gauges, temperature sensors, electrical, magnetic and chemical sensors. Smart sensors.
4. Vibrodiagnostics, electrical diagnostics, thermal diagnostics, tribodiagnostics, noise diagnostics, acoustic emissions, flow and level diagnostics, pressure diagnostics. Non-destructive material testing. Multi-parametric diagnostics. Remote machine diagnostics.
5. Data acquisition from machine and equipment diagnostics, assessment methods. Control diagrams as the tools of statistical process control (SPC). Statistical data analysis, data mining. Recognition of technical diagnostics, statistical methods for classification, clustering, neural networks, diagnostic expert systems, fuzzy systems. Machinery wearing indication, diagnostic monitoring systems, vizualization of diagnostic systems. Technical diagnostics within Industry 4.0 initiative.
6. Preventive maintenance with pre-defined intervals, maintenance according to technical conditions – predictive, pro-active, and testing. Preventive maintenance optimization. System creation and management of preventive, predictive and pro-active maintenance.
7. Maintenance management and engineering – maintenance organization and management in a company (strategy, outsourcing, legislation, planning, schedule, material sources, spare parts, overhauls, and re-engineering), maintenance productivity assessment economics (finance, key indicators, benchmarking), maintenance quality management, (normalized management system, documentation and audit), environmental maintenance management.
8. Maintenance focused on failure free operation – RCM (Reliability Centred Maintenance). Other methods.
9. Total productive maintenance – TPM.
10. Software for maintenance: planning, maintenance management and agenda, management and maintenance of tangible asset and life control, stock holding. Maintenance visualization. Relationship of maintenance, maintenance management and company´s management. Maintenance 4.0 and Industry 4.