This thesis proposes the development of a machine learning-based predictive maintenance
system for electrical panels using thermal data. The system will address the limitations of periodic manual
inspections and enable the detection of anomalies in the operation of electrical devices. By leveraging real-
time thermal data and applying machine learning techniques, the solution aims to enhance the
sustainability and efficiency of maintenance processes, especially in environments like airports where
baggage handling systems (BHS) are critical. This project proposes the use of a 32×32 thermopile sensor
array to collect continuous thermal data and apply machine learning models to predict potential failures
before they occur.
he thermal dataset is provided by Siemens Logistics GmbH.
Problem Statement: Manual thermographic inspections in electrical panels have several limitations,
including the inability to coincide with peak operating times and the reliance on operator expertise to
interpret infrared images. Furthermore, the inability to continuously monitor and analyze the thermal
behavior of electrical panels leads to missed opportunities for early intervention and predictive
maintenance.
Expected Outcomes: The primary outcome of this thesis will be a system that predicts when anomalies
are likely to occur within electrical panels. This will result in fewer manual inspections, minimized
downtime by predicting failures, and recommend inspections for timely maintenance.