Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Enhancing Industrial Equipment Reliability: Advanced Predictive Maintenance Strategies Using Data Analytics and Machine Learning Description An account of the resource In today's dynamic industrial landscape, optimizing machinery performance and minimizing downtime are paramount for sustained operational excellence. This paper presents advanced predictive maintenance strategies, with a focus on leveraging machine learning and data analytics to enhance the reliability and efficiency of industrial equipment. The study explores the key components of predictive maintenance, including data collection, condition monitoring, predictive models, failure prediction, optimized maintenance scheduling and the extension of equipment longevity. The paper discusses how predictive maintenance aligns with modern industrial paradigms. The study evaluated the performance of five popular forecasting models like Random Forest, Linear Regression, Exponential Smoothing, ARIMA, and LSTM, to estimate maintenance for industrial equipment. The effectiveness of each model was evaluated using a number of performance metrics. The percentage of the variation in the real data that the model can explain is shown by the R-squared number. The lowest MSE, RMSE, and greatest R-squared values indicate a model's accuracy. The study highlights practical implications across diverse industries, showcasing the transformative impact of predictive maintenance on minimizing unplanned downtime, reducing maintenance costs, and maximizing the lifespan of critical machinery. When it comes to predictive maintenance for industrial machinery, the LSTM model has been shown to be the most accurate and efficient model with the highest R-squared value, indicating a better fit and higher predictive ability. As technology continues to evolve, the paper discusses future directions, including the integration of artificial intelligence and advanced analytics, and emphasizes the importance of continuous improvement in refining predictive maintenance strategies for the evolving needs of industries worldwide. 2024 IEEE. Creator An entity primarily responsible for making the resource Vincent M.; Thomas S.; Suresh S.; Prathap B.R. Source A related resource from which the described resource is derived Proceedings of InC4 2024 - 2024 IEEE International Conference on Contemporary Computing and Communications Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/InC460750.2024.10649212" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/InC460750.2024.10649212</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203790167&amp;doi=10.1109%2FInC460750.2024.10649212&amp;partnerID=40&amp;md5=b55778b9a08a8619d475881f60df35d5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203790167&amp;doi=10.1109%2fInC460750.2024.10649212&amp;partnerID=40&amp;md5=b55778b9a08a8619d475881f60df35d5</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038365-2 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Vincent M., CHRIST Deemed to be University, Department of Computer Science and Engineering, Bengaluru, India; Thomas S., CHRIST Deemed to be University, Department of Computer Science and Engineering, Bengaluru, India; Suresh S., CHRIST Deemed to be University, Department of Computer Science and Engineering, Bengaluru, India; Prathap B.R., CHRIST Deemed to be University, Department of Computer Science and Engineering, Bengaluru, India