Modern maintenance strategies, such as predictive and prescriptive maintenance, which derived from the concept of Industry and Maintenance 4.0, involve the application of the Industrial Internet of Things (IIoT) to connect maintenance facilities into a unique composition, enabling data collection and analysis which will help make better decisions on maintenance activities. Data collection is the first step and the foundation of any modern maintenance strategy because it collects data that can then be analyzed to provide useful information about the state of maintenance of facilities. The topic of this master thesis is the development of an automated system for collecting maintenance facilities data using IIOT sensors. After a brief introduction to the thesis, the initial part of the paper briefly describes the concept of Industry and Maintenance 4.0 alongside with characteristics of the Industrial Internet of Things. The methodology of the design process is selected, after which the current system and its limitations are described on the basis of which the goals of the new system are set. The central part of the paper describes the system architecture followed by the selection of technical variants of the system components, after which the system processes are described and presented in detail as well as the data models that the system uses to perform tasks. Also, system design is described, which includes system configuration, user interface programming and system testing. The last part of the thesis shows an example of system simulation as well as possible application in the water supply system after which the conclusion is drawn. The development of an automated data collection system has achieved significant savings in the initial costs of system components, enabled remote and automated data collection and provided a basis for further development.