It is quite clear that machine downtime due to a sudden machinery breakdown will cost the organization a lot of money. Organizations need to avoid this by using an innovative maintenance methodology. There are also available machine learning algorithms that can be utilized in the statistical checks to ascertain the primary cause of the problems that were not envisioned due to larger datasets that are available to the companies. The integration of Artificial Intelligence, Machine Learning, and Data Science has emerged as a transformative approach to preventive mechanical maintenance, offering profound enhancements in the reliability and operational efficiency of industrial machinery. The author’s aim is to predict the failure of mechanical components using Artificial Intelligence, Machine Learning, and Data Science in Mechanical Maintenance, with a particular focus on milling machine components, including the boom roller, copping roller, guide roller, and support material device. We used different supervised machine learning algorithms like Linear Regression, Gradient Boosting, Random Forest, Decision Tree, K-nearest neighbors, and Support Vector Machine. The findings reveal that the Support Vector Machine model delivers the highest accuracy than other algorithms, predicting failures with precision rates of 75% for the boom roller, 63.64% for the copping roller, 53.85% for the guide roller, and an impressive 69.23% for the Support Material Device. Additionally, the Mean Absolute Error analysis for the Support Vector Machine model indicates minimal prediction errors of 1 to 4 days. This research highlights the tangible benefits of implementing Artificial Intelligence-driven predictive maintenance in industrial settings, including cost savings, improved machinery performance, and enhanced safety standards.

Artificial intelligence, Data science, Machine learning, Predictive maintenance, Python, Data analysis, Data visualization, Mean absolute error

PdM stands as one of the factor strategies that rely on real-time data to predict machine failures by estimating the RUL. This approach is important for industrial machines where safety takes priority due to the huge costs and potential risk to human life safety (Adryan and Sastra 2021). In conclusion, the project underscores the critical role of AI, ML, and Data Science in modernizing predictive maintenance strategies. By adopting these technologies, industries can move towards proactive maintenance practices that not only enhance reliability and safety but also drive significant cost efficiencies. As technology continues to evolve, integrating advanced analytics and predictive capabilities will remain pivotal in shaping the future of mechanical maintenance. After analyzing components' real-time data with the help of Python and its libraries like NumPy, Pandas, scikit-learn, Matplotlib, sklearn. Linear_model and using several machine learning algorithms like Linear Regression, Gradient Boosting, Random Forest, Decision Tree, K- Nearest Neighbors, Support Vector Machine. We can conclude that the SVM is the best-suited algorithm to predict the component failure date of the milling machine. SVM achieved the highest accuracy rates among all other algorithms that are tested, which is 75% for boom roller, 63.64% for copping roller, 53.85% for guide roller, and an impressive 69.23% for SMD. MAE shows the average difference between the predicted date by algorithms and the actual failure date of the component. The MAE shown by SVM is ±0.67 days for the boom roller, ±1.36 days for the copping roller, ±1.38 days for the guide roller, and ±3.65 days for SMD. When analyzing parameters for predictive maintenance, common factors such as temperature, speed, and vibration are often prioritized. These are critical indicators of a machine's performance and can provide valuable insights into the likelihood of component failure. However, it is important to recognize that other factors, like lubrication, also play a crucial role in forecasting the failure of components. Proper lubrication reduces friction, minimizes wear, and ensures the smooth operation of mechanical parts. A lack of adequate lubrication can lead to overheating, increased wear, and eventual breakdown, making it a significant factor to consider in predictive maintenance strategies. While gathering real-time data, machines sometimes undergo modifications to increase their capacity and production output. During these periods of modification, the machine may not operate as expected, which complicates the process of analyzing data and predicting component failures. The altered performance metrics during modifications can lead to inaccurate data, making it challenging to assess the true condition of the machine and its components. Consequently, predictive maintenance efforts may be hindered, as the irregular data does not reflect the machine's typical operating conditions. Future work in the field of predictive mechanical maintenance will likely focus on further enhancing the accuracy and efficiency of AI and ML models. One of the major challenges in creating a predictive maintenance system is the lack of failure data, as the machine/ components are frequently repaired before they fail (Van Dinter et al., 2022). As data collection becomes more sophisticated, incorporating real-time data from IoT devices and advanced sensors will provide richer datasets for analysis. This will enable the development of more precise predictive models.

Suraj Shashikant Patil and Gajanan N. Thokal (2025). Analysis of Predictive Mechanical Maintenance Using Artificial Intelligence, Machine Learning and Data Science. International Journal on Emerging Technologies, 16(1): 45–53.