Gourav Vivek Kulkarni, Gopalakrishna H D, Akshath Anand Ghanti | International Journal of Computer Aided Manufacturing | Vol 10, Issue 02 | pp. 19-32
Abstract
The Internet of Things (IoT), the use of machine learning (ML), and artificial intelligence (AI)-driven advances in predictive maintenance are the key topics of this study. With the goal of offering a thorough grasp of existing approaches and potential future developments, it analyzes applications in predictive maintenance in many sectors. Predictive maintenance approaches are the major topic of discussion, with an emphasis on their advantages, disadvantages, difficulties, and prospects. Additionally, it assesses ordinary algorithms and machine learning libraries, highlighting the need of selecting frameworks according to project specifications and efficiency concerns. Random Forest, K-Neighbors, and the support vector machine are among the algorithms that are compared; the Random Forest model is selected for more examination. Understanding feature significance and dataset characteristics is supplied by the provided Python programs, enabling data analysis and prediction using a RandomForestClassifier model. The results have implications for a number of real-world applications and point to possibilities for future study, including time-series analysis, ensemble learning, hyperparameter tweaking, and sophisticated feature engineering to improve predictive modeling performance.
Keywords: Predictive, Maintenance, Algorithm, Milling, Power
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How to cite this article
@article{KulkarniGV2024,
author = {Gourav Vivek Kulkarni and Gopalakrishna H D and Akshath Anand Ghanti},
title = {Advancements in Predictive Maintenance: A Review of Data-Driven Approaches, Machine Learning, and AI in Diverse Industrial Landscapes},
journal = {International Journal of Computer Aided Manufacturing},
year = {2024},
volume = {10},
number = {02},
pages = {19--32},
url = {https://journalspub.com/publication/ijcam/article=13702}
}