Apeiranthitis, S.; Zacharia, P.; Chatzopoulos, A.; Papoutsidakis, M. Predictive Maintenance of Machinery with Rotating Parts Using Convolutional Neural Networks. Electronics2024, 13, 460.
Apeiranthitis, S.; Zacharia, P.; Chatzopoulos, A.; Papoutsidakis, M. Predictive Maintenance of Machinery with Rotating Parts Using Convolutional Neural Networks. Electronics 2024, 13, 460.
Apeiranthitis, S.; Zacharia, P.; Chatzopoulos, A.; Papoutsidakis, M. Predictive Maintenance of Machinery with Rotating Parts Using Convolutional Neural Networks. Electronics2024, 13, 460.
Apeiranthitis, S.; Zacharia, P.; Chatzopoulos, A.; Papoutsidakis, M. Predictive Maintenance of Machinery with Rotating Parts Using Convolutional Neural Networks. Electronics 2024, 13, 460.
Abstract
All kinds of vessels consist of dozens of complex machineries with rotating parts and electric motors that operate continuously in a harsh environment with excess temperature and humidity, vibration, fatigue and load. A breakdown or malfunction in one of these machineries can significantly impact the vessel’s operation and safety and consequently, the safety of the crew and the environment. To maintain operational efficiency and seaworthiness, the shipping industry invests substantial resources in preventive maintenance and repairs. This research presents the economic and technical benefits of predictive maintenance over to traditional preventive maintenance, and repair by replacement approaches in the maritime domain. By leveraging modern technology and Artificial Intelligence, we can analyze real-time operating conditions of machinery, enabling early detection of potential damages and allowing for effective planning of future maintenance and repair activities. In this paper, we propose and develop a Convolutional Neural Network that is fed with raw vibration measurements acquired in a laboratory environment from the ball bearings of a motor. Then, we investigate whether the proposed network can accurately detect the functional state of ball bearings and categorize any possible failures present, contributing to improved maintenance practices in the shipping industry.
Keywords
predictive maintenance; convolutional neural network; deep learning; vibration
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
