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Predictive Analytics Module Development for Industrial Equipment Diagnostic System

Student: Novikov Nikita

Supervisor: Aleksey Kychkin

Faculty: Faculty of Economics, Management, and Business Informatics

Educational Programme: Business Informatics (Bachelor)

Year of Graduation: 2020

The present paper provides a phased description of the development of a predictive analytics module for a process equipment diagnostic system. The development was carried out on the basis of a preliminary study and analysis of technological maintenance and repair processes, approaches to diagnosing the condition of equipment. Based on the analysis of existing systems, as well as taking into account a deep theoretical analysis of the processes, the requirements for the module were formulated. Based on them, a predictive analytics module was developed and subsequently tested. The module allows you to work with time series data, load data from a file, and get it from the InfluxDB database. Based on these data, using the built-in forecasting algorithm and selecting the best parameters, a forecast of future values ​​is made. The forecast is visualized on a chart along with real data. The visualization is interactive, and a report is built in the module that notifies the user if there are accidents in the forecast and reports the time of their occurrence. The work consists of introduction, three chapters, conclusion, bibliography, containing 18 sources and 5 applications. The work is presented on 97 pages, contains 39 figures and 2 tables.

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