Accurate categorization of components based on part numbers is a fundamental task for reliability engineers, essential for assessing and improving the reliability of systems. Manual categorization is labor-intensive and prone to errors, highlighting the need for an automated approach. This thesis presents a machine learning classifier designed to predict component categories from part numbers, with the goal of enhancing the efficiency of Integrated Logistic Support. The proposed solution utilizes TF-IDF vectorization combined with classifiers such as Multinomial Naive Bayes, Stochastic Gradient Descent and Linear Support Vector Classifier, enabling the model to effectively analyse and categorize part numbers. An interactive graphical user interface facilitates user input and provides immediate predictions, thereby streamlining the categorization process for reliability engineers. This ML-driven tool not only reduces the manual effort required, but also enhances the precision of component categorization, leading to better reliability assessments and system evaluations. The research demonstrates the potential of ML in automating complex engineering tasks and suggests pathways for future enhancements, including the integration of additional component attributes and validation in diverse real-world scenarios. The ultimate goal is to create a robust tool that can be widely adopted in the field of reliability engineering, thereby optimizing workflows and improving overall system reliability.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-532612 |
| Date | January 2024 |
| Creators | Mian, Faizan |
| Publisher | Uppsala universitet, Avdelningen för systemteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC IT, 1401-5749 ; 24009 |
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