Predictive Maintenance in HVAC System utilizing Machine Learning

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/302895
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Type: Examensarbete för masterexamen
Title: Predictive Maintenance in HVAC System utilizing Machine Learning
Authors: Abdulle, Ibrahim
Dang, Richard
Abstract: The fourth industrial revolution is present in today’s landscape of industrial engi- neering and digitalization has emerged to be a vital part of an organization’s product portfolio. Industry 4,0 endorses companies an opportunity to make a superior in- formed fact-based decision. Digitalization and creating more data-driven decision making is considered to be lucrative and innovative enough to push organisations a step closer to Industry 4,0. Swegon aims to investigate if it is feasible to implement a predictive type of main- tenance to forecast when the wreckage is approaching in the HVAC systems. To guide Swegon AB closer to the ideal Industry 4,0, a current situation analysis was conducted to examine if the predictive type of maintenance is viable on Swegon current data by utilizing Machine Learning. A collaboration of Cross-Industry Process for Data Mining (CRISP-DM) and Prod- uct Development methodologies have been utilized to prepare and create an under- standing of the input data, to build the Machine Learning model based on input data from Swegon and also to measure the overall potential of the input data. An analysis of the Machine Learning model was conducted and this resulted in several recommendations for not only Swegon but every company trying to implement pre- dictive maintenance using machine learning to continue pushing the organisation closer towards Industry 4.0 and to accomplish a predictive type of maintenance
Keywords: Predictive Maintenance, HVAC System, machine learning, wreckage
Issue Date: 2021
Publisher: Chalmers tekniska högskola / Institutionen för industri- och materialvetenskap
URI: https://hdl.handle.net/20.500.12380/302895
Collection:Examensarbeten för masterexamen // Master Theses (IMS)



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