The use of machine learning for predictive maintenance has been the focus of many studies, usually utilizing industrial setups consisting of actual industrial motors. This work examines the possibility of creating a simple setup to develop a machine learning model to detect electric motor failures, eliminating the need to rely on having access to industrial equipment in the early stages. The work conducted in this thesis leverages autoencoders, a specific type of neural network, to detect motor faults based on vibration readings from an accelerometer. The final model detected anomalies with 100% accuracy at three different speeds when a constant load was applied to the motor. However, it should be improved when a variation in load is introduced as it only had 85.1% Accuracy and 90.1% F1-score with 82.0% Recall and 99.8% Precision. In conclusion, the setup and the model developed show promise as an initial setup for testing and experimenting with electric motors and machine learning for predictive maintenance.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kau-90224 |
| Date | January 2022 |
| Creators | Qasem, Mohammad |
| Publisher | Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), Karlstads universitet, Avdelningen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf, application/pdf |
| Rights | info:eu-repo/semantics/openAccess, info:eu-repo/semantics/openAccess |
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