  Factor analysis or sometimes referred to as variable analysis has been
extensively used in classification problems for identifying specific factors
that are significant to particular classes. This type of analysis has been
widely used in application such as customer segmentation, medical research,
network traffic, image, and video classification. Today, factor analysis is
prominently being used in fault diagnosis of machines to identify the
significant factors and to study the root cause of a specific machine fault.
The advantage of performing factor analysis in machine maintenance is to
perform prescriptive analysis (helps answer what actions to take?) and
preemptive analysis (helps answer how to eliminate the failure mode?). In this
paper, a real case of an industrial rotating machine was considered where
vibration and ambient temperature data was collected for monitoring the health
of the machine. Gaussian mixture model-based clustering was used to cluster the
data into significant groups, and spectrum analysis was used to diagnose each
cluster to a specific state of the machine. The significant features that
attribute to a particular mode of the machine were identified by using the
random forest classification model. The significant features for specific modes
of the machine were used to conclude that the clusters generated are distinct
and have a unique set of significant features.
