  Predictive maintenance and condition-based monitoring systems have seen
significant prominence in recent years to minimize the impact of machine
downtime on production and its costs. Predictive maintenance involves using
concepts of data mining, statistics, and machine learning to build models that
are capable of performing early fault detection, diagnosing the faults and
predicting the time to failure. Fault diagnosis has been one of the core areas
where the actual failure mode of the machine is identified. In fluctuating
environments such as manufacturing, clustering techniques have proved to be
more reliable compared to supervised learning methods. One of the fundamental
challenges of clustering is developing a test hypothesis and choosing an
appropriate statistical test for hypothesis testing. Most statistical analyses
use some underlying assumptions of the data which most real-world data is
incapable of satisfying those assumptions. This paper is dedicated to
overcoming the following challenge by developing a test hypothesis for fault
diagnosis application using clustering technique and performing PERMANOVA test
for hypothesis testing.
