AI-Driven Predictive Maintenance in Mining: A Systematic Literature Review on Fault Detection, Digital Twins, and Intelligent Asset Management
The mining industry faces increasing challenges in maintaining high production levels while minimizing unplanned failures and operational costs. Critical assets, such as crushers, conveyor belts, mills, and ventilation systems, operate under extreme conditions, leading to accelerated wear and failur...
Saved in:
| Main Authors: | Luis Rojas, Álvaro Peña, José Garcia |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/6/3337 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Survey on Data Mining for Data-Driven Industrial Assets Maintenance
by: Eduardo Coronel, et al.
Published: (2025-02-01) -
Machine Learning-Based Predictive Maintenance at Smart Ports Using IoT Sensor Data
by: Sheraz Aslam, et al.
Published: (2025-06-01) -
Predictive Maintenance in IoT-Monitored Systems for Fault Prevention
by: Enrico Zero, et al.
Published: (2024-09-01) -
Human-Centric IoT-Driven Digital Twins in Predictive Maintenance for Optimizing Industry 5.0
by: Özlem Sabuncu, et al.
Published: (2025-06-01) -
SET: A Shared-Encoder Transformer Scheme for Multi-Sensor, Multi-Class Fault Classification in Industrial IoT
by: Kamran Sattar Awaisi, et al.
Published: (2025-01-01)