Advanced Failure Classification Models for Construction Machinery: A Case Study with Volvo CE : Integrating Machine Learning to Reduce Downtime and Operational Costs

Sammanfattning

This thesis looks at how artificial intelligence (AI) and machine learning (ML) can be used together to create better ways to classify failures in construction equipment, especially Volvo Construction Equipment (VCE). To keep machine downtime and operational costs as low as possible while dealing with the problems caused by class imbalances in the datasets, the goal is to switch from reactive to predictive strategies. Objectives The primary objective of this research is to design and evaluate sophisticated machine learning algorithms that analyze sensor data to classify potential failure types. The study also wants to find out how well deep learning models, like transformer-based architectures, work and how data balancing techniques can make failure classification systems more reliable. Methods: The research employs a quantitative analytical framework using real-world performance datasets from manufacturing equipment. We tested three different methods: (1) using unbalanced raw data as a starting point; (2) using SMOTE to balance the dataset, including a limited version for multiclass classification; and (3) using binary classification on the sampled data from Volvo CE. These methods enabled the exploration of the effects of data balancing on model performance and interpretability. Results The experiments highlighted the challenges posed by class imbalance and its adverse effects on the accuracy and reliability of the model. SMOTE significantly improved precision, recall, and F1 scores for underrepresented failure types. However, rare failure modes still present unresolved challenges. Transformer-based architectures demonstrated notable accuracy improvements, especially when combined with balanced datasets. Conclusions This study shows how important it is to fix class imbalances in failure classification datasets to make models more reliable and improve how well they work. The findings contribute to the advancement of AI-driven failure classification in the construction industry, paving the way for proactive maintenance strategies that reduce downtime and optimize costs.