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Sistem porazdeljenih spletnih storitev za proizvodno analitiko
GAZVODA, MIHA (Author), Mušič, Gašper (Mentor) More about this mentor... This link opens in a new window, Glavan, Miha (Co-mentor)

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Abstract
Podjetja v proizvodnji postavljajo senzorje in si z analizo podatkov želijo pridobiti konkurenčno prednost - nove vpoglede, hitrejšo, kvalitetnejšo in cenejšo proizvodnjo. Shranjevanje velikih količin podatkov je postalo preprosto in ekonomsko dostopno. Vse to žene povpraševanje po pripomočkih za analitične algoritme, ki bi bili primerni za vgradnjo v avtomatizirane procese in dovolj enostavni za uporabo kadru v proizvodnji. V magistrski nalogi je predstavljen pristop za integracijo različnih okolij in algoritmov podatkovne znanosti v enoten sistem spletnih storitev, ki ga implementira podatkovni analitik. Razvite storitve omogočajo hitro integracijo in preprosto uporabo, primerno za domenske eksperte (proizvodne tehnologe). Končni uporabnik tako ne potrebuje ekspertnega analitičnega znanja in poznavanja pripadajočih orodij. Storitve se lahko izvajajo v oblaku, kar pomeni zmogljivejšo strojno opremo, dostop z različnih naprav in posledično pohitritev procesa - tudi zaradi hitrejše implementacije in enostavnejše uporabe. Razvite storitve lahko uporabnik kliče neposredno, do njih dostopa preko uporabniškega vmesnika, ali pa se storitve integrirajo v avtomatiziran proces. Zaradi enostavne uporabe in možnosti integracije so storitve primerne in v prvi vrsti namenjene za vodenje proizvodnje. Algoritmi, na katerih temeljijo razvite spletne storitve, so implementirani v programskih jezikih Python in Matlab. Spletne storitve so razvite z orodjem Swagger ali v okolju Microsoft Azure Machine Learning Studio (Azure ML). Tečejo na oblaku Microsoft Azure ali lokalno. Odjemalci so implementirani v programskih jezikih oziroma okoljih Java, Python, Microsoft Excel, Matlab ali Azure ML.

Language:Slovenian
Keywords:podatkovna znanost, spletna storitev, proizvodna analitika, vodenje proizvodnje
Work type:Master's thesis/paper (mb22)
Organization:FE - Faculty of Electrical Engineering
Year:2019
Views:68
Downloads:61
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Secondary language

Language:English
Title:Distributed system of web services for production analytics
Abstract:
In order to get a competitive advantage, the number of sensors in production is rising rapidly. Companies are using data analysis to gain new insights, speed up the production, lower the price and make products with better quality. The storage of large amounts of data has become easier and economically more accessible. This raises the demands for development of analytical algorithms that are suitable to embed into automated processes and are easy to use. This master's thesis introduces an approach for the integration of different data science environments and algorithms into a system of web services. This concept enables fast integration and is easy to use. It is suitable for domain experts (production technologists). The end user thus doesn't need expert analytical knowledge or the knowledge of associated tools. Services can be implemented in the cloud, which means more powerful hardware and access from different devices. With faster deployment and ease of use, the process is sped up considerably. The developed services can be used directly by the user, accessed through the user interface, or integrated into the automated process. Due to this integration possibilities, the services are suitable and are primarily intended for production control. The algorithms are implemented in programming languages Python and Matlab. Web services are developed with the tool Swagger or in the Microsoft Azure Machine Learning Studio (Azure ML) cloud environment. They run on the Microsoft Azure cloud or are implemented locally. Clients are implemented in programming languages Java, Python, Matlab, Azure ML environment and Microsoft Excel spreadsheet.

Keywords:data science, web service, production analytics, production control

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