This leads to unscheduled outages that cost companies
millions of dollars each year. Traditional maintenance strategies can be applied, but they provide insufficient
warning of impending failures. On the other hand, condition monitoring and on-line assessment of
the wear status of wetted components in slurry pumps are expected to improve maintenance management
and generate significant cost savings for pump operators. In this context, the objective of the present
work is to develop and compare two unsupervised clustering ensemble methods, i.e., fuzzy C-means
and hierarchical trees, for the assessment and measurement of the wear status of slurry pumps when
available data is extremely limited. The idea is to combine predictions of multiple classifiers to reduce
the variance of the results so that they are less dependent on the specifics of a single classifier. This will
also reduce the variance of the bias, because a combination of multiple classifiers may learn a more
expressive concept class than a single classifier.