Deep Learning for Machine Health Prognostics: A Case Study in Power Generation Systems

Authors

  • Min-jun Kim Department of Computer and Information Engineering, Catholic University of Pusan, Busan 46252, Republic of Korea Author
  • Ji-hoon Park Department of Computer and Information Engineering, Catholic University of Pusan, Busan 46252, Republic of Korea Author
  • Seung-hyun Lee Department of Computer and Information Engineering, Catholic University of Pusan, Busan 46252, Republic of Korea Author

Abstract

In recent years, the integration of deep learning technologies into prognostics and health management (PHM) has revolutionized the way we assess the health and performance of industrial systems, particularly in power generation. This innovative approach leverages vast amounts of sensor data to monitor equipment health, predict potential failures, and optimize maintenance schedules, ultimately reducing costs and enhancing operational efficiency. This article delves into the critical role of deep learning in machine health prognostics, focusing on its applications in power generation systems.

Downloads

Published

2022-07-02

Issue

Vol. 3 No. 2 (2022): IJCRL

Section

Articles

How to Cite

Deep Learning for Machine Health Prognostics: A Case Study in Power Generation Systems. (2022). International Journal of Contemporary Research and Literacy Works, 3(2), 1-8. https://ijcrl.com/1/article/view/17