| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 1.19 MB | Adobe PDF |
Authors
Advisor(s)
Abstract(s)
Industry traditionally considered maintenance as a cost and a necessity to replace equipment and machines, but the path has changed to better focus on maintenance to prevent faults and it was designated as predictive. The ones motivated to take these advantages are faced with two of the biggest barriers: the investment it requires and the difficulty to develop algorithms. The costs of installation are still high, but the avoided costs surpass it. Also, Internet of Things (IoT) has brought a big shift, which is been known as the industry 4.0. One of the potentials in maintenance is the conditioning monitoring. Condition monitoring sensors and devices are now linked to maintenance platforms, providing real-time data. This new connectivity is both more affordable and easier to implement than predictive maintenance. Real-time data allows managers to adjust preventive maintenance plans while providing greater reliability. At the same time, artificial intelligence manages this data to recognize patterns, which is one of the most promising advances in digital reliability. So, regardless of the ability to immediately implement a preventive maintenance plan, condition monitoring is an asset itself. The present paper presents the common faults on electric machines, their effects, their impact on the industry and the main techniques on condition control to prevent them. It is also added the reflection on the use of IoT to enhance the potential of condition control maintenance. The implementation of continuous improvement actions throughout the life of the equipment allows to increase efficiency, either by overcoming weaknesses or by adapting production or operational capacities to processes, production or maintenance, avoiding under maintenance or over maintenance and minimizing operating costs.
Description
Keywords
Condition monitoring Condition control Damage detection Vibration analysis Model-updating
Citation
DOMINGUES, Nuno – Industry 4.0 in maintenance: Using condition monitoring in electric machines. In 2021 International Conference on Decision Aid Sciences and Application (DASA). Sakheer, Bahrain: IEEE, 2022. ISBN 978-1-6654-1634-4. Pp. 1-7.
Publisher
IEEE