1. Introduction
Predictive maintenance transforms manufacturing operations...
2. Data Collection
Sensor networks and data acquisition...
3. Machine Learning Models
Random Forest, LSTM, and ensemble methods...
4. Results
Performance comparison and cost analysis...
5. Implementation
Deployment and practical considerations...
References
[1] Lee, J. et al. (2023). Industry 4.0.
[2] Davis, M. (2024). Smart Manufacturing.
Machine Learning for Predictive Maintenance in Manufacturing
Emma Brown
Oxford University
DOI: 10.1234/journal.v2.2.010
Published: October 15, 2024
Research Articles
Abstract
This paper presents a machine learning approach for predictive maintenance in manufacturing environments. Using sensor data from 200 industrial machines over 2 years, we developed models that predict equipment failures with 92% accuracy. The system reduces downtime by 40% and maintenance costs by 25%.
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Article Info
-
Issue:
Vol. 2 No. 1 (2024): Summer Edition -
Section:
Research Articles -
Submitted:
August 01, 2024 -
Published:
October 15, 2024 -
DOI:
10.1234/journal.v2.2.010
How to Cite
Emma Brown (2024). "Machine Learning for Predictive Maintenance in Manufacturing." Abhidakara Jurnal, 2(1). doi:10.1234/journal.v2.2.010