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

Document Type : Original Article

Authors

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.

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