USING MULTIVARIATE LATENT MODELS TO MONITOR A PRINTING MACHINE AND PREDICT MACHINE FAILURE
M., S., M., H., M., S., I., M. (2008). USING MULTIVARIATE LATENT MODELS TO MONITOR A PRINTING MACHINE AND PREDICT MACHINE FAILURE. EKB Journal Management System, 13(13th International Conference on Applied Mechanics and Mechanical Engineering.), 49-62. doi: 10.21608/amme.2008.38997
SAID E. M.; HUSSEIN W. M.; SALEM A. M.; MOHAMED K. I.. "USING MULTIVARIATE LATENT MODELS TO MONITOR A PRINTING MACHINE AND PREDICT MACHINE FAILURE". EKB Journal Management System, 13, 13th International Conference on Applied Mechanics and Mechanical Engineering., 2008, 49-62. doi: 10.21608/amme.2008.38997
M., S., M., H., M., S., I., M. (2008). 'USING MULTIVARIATE LATENT MODELS TO MONITOR A PRINTING MACHINE AND PREDICT MACHINE FAILURE', EKB Journal Management System, 13(13th International Conference on Applied Mechanics and Mechanical Engineering.), pp. 49-62. doi: 10.21608/amme.2008.38997
M., S., M., H., M., S., I., M. USING MULTIVARIATE LATENT MODELS TO MONITOR A PRINTING MACHINE AND PREDICT MACHINE FAILURE. EKB Journal Management System, 2008; 13(13th International Conference on Applied Mechanics and Mechanical Engineering.): 49-62. doi: 10.21608/amme.2008.38997

USING MULTIVARIATE LATENT MODELS TO MONITOR A PRINTING MACHINE AND PREDICT MACHINE FAILURE

The International Conference on Applied Mechanics and Mechanical Engineering
Article 19, Volume 13, 13th International Conference on Applied Mechanics and Mechanical Engineering., May 2008, Page 49-62  XML PDF (551.41 K)
Document Type: Original Article
DOI: 10.21608/amme.2008.38997
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Authors
SAID E. M.; HUSSEIN W. M.; SALEM A. M.; MOHAMED K. I.
Egyptian Armed Forces.
Abstract
ABSTRACT
Condition-Based Monitoring involves continuous collection and interpretation of data
relating to the operating conditions of critical components of the machine. To ensure
high printing quality while maximizing productivity, an on-line process monitoring
system is required to take the place of an expert’s judgment. This paper outlines the
use of a statistical multivariate technique called Principal Component Analysis (PCA),
as a health monitoring technique, and applies it to monitor a GTO type of printing
machine. This approach is used to integrate vibration data taken at different positions
and directions on the printing machine. Experiments were conducted on the machine
for different operating speeds under two conditions, new and worn drive belt. The
results showed that the proposed technique can be used for printing machine
monitoring and can successfully differentiate between new process conditions.
Keywords
Printing monitoring; Multivariate Analysis; Principal component analysis; Vibration monitoring
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