INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Optimizing Machine Performance and Reliability: A Predictive Maintenance Approach
Authors Name:
Satwik Gupta
, Shubham , Sarthak Joshi , AK Madan
Unique Id:
IJSDR2305088
Published In:
Volume 8 Issue 5, May-2023
Abstract:
The research work focuses on the application of predictive maintenance for machines using the AI4I 2020 Predictive Maintenance Dataset. Predictive maintenance is a data-driven approach to optimize the maintenance of machines and minimize unexpected downtime. The study aims to explore the potential of AI and machine learning techniques in predictive maintenance and evaluate the performance of these algorithms on the AI4I 2020 Predictive Maintenance Dataset. The results of the study show the effectiveness of predictive maintenance in increasing machine efficiency and reducing maintenance costs. The findings also highlight the importance of choosing the appropriate algorithm and feature selection techniques for predictive maintenance tasks. The study provides valuable insights for practitioners and researchers in the field of predictive maintenance and demonstrates the potential of AI and machine learning techniques in optimizing the maintenance of machines.
Keywords:
Cite Article:
"Optimizing Machine Performance and Reliability: A Predictive Maintenance Approach", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 5, page no.611 - 614, May-2023, Available :http://www.ijsdr.org/papers/IJSDR2305088.pdf
Downloads:
000346989
Publication Details:
Published Paper ID: IJSDR2305088
Registration ID:206037
Published In: Volume 8 Issue 5, May-2023
DOI (Digital Object Identifier):
Page No: 611 - 614
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
Facebook Twitter Instagram LinkedIn