Abstract

Predictive maintenance was recently introduced as a paradigm shift in the field of smart manufacturing, and it allows organizations to predict equipment failures and streamline maintenance planning based on the data-driven insights. The present work addresses the concept of the implementation of a decision tree-based predictive maintenance model based on AI4I 2020 Predictive Maintenance Dataset, a simulated environment of an actual industry through sensor data including temperature, torque, rotational velocity, and toolwear. The study uses a quantitative approach that is based on positivism philosophy, which is the focus on objectivity, empirical validation, and statistical testing of the model performance. Synthetic Minority Oversampling Technique (SMOTE) was used to solve the problem of skewed data in the dataset wherein the distribution has been equalized between the failure and non-failure cases. This preprocessing procedure raised the ability of the model to identify minority-class events, such as machine failures, and this negatively affected the recall and the overall classification accuracy. The Decision Tree classifier was obtained with the accuracy of 90.76 percentage backed up by the precision, recall, and F1-score values that show good prediction performance and at the same time, interpretability which is imperative in any industrial use. The feature-importance analysis showed that the variables concerning Heat Dissipation Failure, Power Failure, and Overstrain Failure were the most significant variables that predict equipment breakdowns. The results of the study indicate that the combination of machinelearning interpretability and balanced and high-quality data is essential in the process of obtaining reliable predictive maintenance solutions. The findings confirm the Decision Tree as a realistic and explicable decision model to use in the practical application and provides transparency as well as accuracy in the maintenance decision making. The study culminates in suggestions on how continuous monitoring can be introduced, sensor networks can be extended, and real time earlywarning dashboards can be developed to make industries efficient and resilient to operations. The next steps inwork are to investigate the techniques of ensemble learning and hybrid predictive systems to enhance further the accuracy of fault-detection and adaptability to a tough manufacturing environment.

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research (Dubai)

Advisor

Sanjay Modak

Advisor/Committee Member

Parthasarathi Gopal

Campus

RIT Dubai

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