Predictive Maintenance Based on Big Data Analytics in Modern Industrial Applications

Predictive maintenance is a powerful maintenance strategy that makes possible to reduce operation and maintenance costs of industrial facilities. It is a complex data- driven process, which tries to forecast future states of company assets. On one hand it prerequisites condition monitoring of components on machine level. On the other it demands the integration of the collected data with other management information systems. Digitalization and especially the advent of big data science bring along promising opportunities to create effective predictive maintenance application. The aim of this master thesis is to examine the possibilities of a predictive maintenance framework based on the design principles of Industry 4.0. It introduces numerous enabling technologies such as industrial internet of things, standardized communication protocols or even edge and cloud computing. Moreover, it takes a deeper look at data analytical techniques and frameworks, and it analyses the possibilities of well-known machine learning algorithms. Finally, it attempts to propose an architecture of a predictive maintenance framework utilizing existing software solutions.