Abstract

With the developments of industry 4.0, data analytics solutions and their applications have become more prevalent in the manufacturing industry. Currently, the typical software architecture supporting these solutions is modular, using separate software for data collection, storage, analytics, and visualization. The integration and maintenance of such a solution requires the expertise of an information technology team, making implementation more challenging for small manufacturing enterprises. To allow small manufacturing enterprises to more easily obtain the benefits of industry 4.0 data analytics, a full-stack data analytics framework is presented and its performance evaluated as applied in the common industrial analytics scenario of predictive maintenance. The predictive maintenance approach was achieved by using a full-stack data analytics framework, comprised of the PTC Thingworx software suite. When deployed on a lab-scale factory, there was a significant increase in factory uptime in comparison with both preventative and reactive maintenance approaches. The predictive maintenance approach simultaneously eliminated unexpected breakdowns and extended the uptime periods of the lab-scale factory. This research concluded that similar or better results may be obtained in actual factory settings, since the only source of error on predictions would not be present in real world scenarios.

Degree

MS

College and Department

Ira A. Fulton College of Engineering and Technology; Mechanical Engineering

Rights

https://lib.byu.edu/about/copyright/

Date Submitted

2022-06-02

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd12362

Keywords

data analytics, machine learning, manufacturing, industry 4.0, industrial internet of things, predictive maintenance

Language

english

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