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 Book Chapter Book Chapter Faculty Publications- Book Chapter 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 Using Ensemble Model to Reduce Downtime in Manufacturing Industry: An Advanced Diagnostic Framework for Early Failure Detection Description An account of the resource The fourth industrial resolution marks a significant shift that uses emerging technologies such as intelligent automation, extensive machine-to-machine communication, and the internet of things (IoT) to modernize conventional manufacturing and industrial methods. The examination of vast data gathered in modern industrial facilities has not only greatly leveraged artificial intelligence (AI) tools but has also driven the development of innovative technologies. In this context, a novel framework for predictive maintenance in the production sector is introduced in this research, which depends on an ensemble model. First, a set of input features are collected from sensors. Then, data normalization technique is applied to standardize and prepare data for further analysis. These normalized input features are then used to train an ensemble classifier. In the ensemble model, multilayer perceptron (MLP), K-nearest neighbors (KNN), and support vector machine (SVM) are serve as base classifiers. Efficacy of the designed framework is validated using predictive maintenance dataset. Results demonstrated that the proposed ensemble model exhibited improved accuracy compared to individual base classifiers. The results further demonstrated that the implemented model had superior efficiency compared to the other benchmark models. 2025 selection and editorial matter, Amit Kumar Tyagi, Shrikant Tiwari, and Gulshan Soni; individual chapters, the contributors. Creator An entity primarily responsible for making the resource Thenmozhi S.; Kumudavalli M.V.; Karthikeyan P.; Kumar C.S.; Sharmila S. Source A related resource from which the described resource is derived Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing, pp. 184-196. Publisher An entity responsible for making the resource available CRC Press 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/10.1201/9781003480860-10" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003480860-10</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207536539&amp;doi=10.1201%2F9781003480860-10&amp;partnerID=40&amp;md5=8bf2c965d799c893f21b73b022277955" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207536539&amp;doi=10.1201%2f9781003480860-10&amp;partnerID=40&amp;md5=8bf2c965d799c893f21b73b022277955</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-104015139-6; 978-103276952-3 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 Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Thenmozhi S., Dayanandasagar College of Engineering, Karnataka, Bengaluru, India; Kumudavalli M.V., Dayanandasagar College of Engineering, Karnataka, Bengaluru, India; Karthikeyan P., Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan; Kumar C.S., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Sharmila S., Dayanandasagar College of Engineering, Karnataka, Bengaluru, India