A study on predictive maintenance using edge intelligence
Typ
Examensarbete för masterexamen
Program
Production engineering (MPPEN), MSc
Publicerad
2022
Författare
Kolisetty, Sai Bharath
Pacha, Praveen
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Production systems have been following a reactive type of approach for maintenance
activities, but in recent times, the interest has shifted towards the development of
more proactive approaches to avoid failures. This has resulted in increased indus trial data collection through the deployment of information and communication
technologies. With this advent, there is also an increase in Predictive Maintenance
(PdM) solutions. Developing Machine Learning (ML) models to perform PdM ac tivities have become a recent challenge and a popular research area under Industry
4.0. Therefore, this thesis takes its roots from this emerging area of data-driven de cision making for PdM with the support of key enabling technologies of Industry 4.0.
This thesis aims to analyze high-dimensional data coming from Edge devices and
investigate what type of predictive algorithms can be designed to implement PdM
at the industrial level. Data collected by the Edge device from two Manufactur ing companies were analyzed and different ML models were developed for both
cases. Further, a comparative study between the cases was presented to show the
importance of faulty data to develop a better ML model for PdM. Finally, some
recommendations were provided for the successful implementation of the ML model
in both companies by using the advantages of the Edge device. This provides a
concrete platform for future research in the area of handling missing data and im plementation of Edge Intelligence in a manufacturing setup.
Beskrivning
Ämne/nyckelord
Edge intelligence, Faulty data, Machine learning, Manufacturing, Predictive Maintenance, Smart Maintenance