Fault Diagnosis using Clustering. What Statistical Test to use for Hypothesis Testing?

TL;DR

This paper explores the use of clustering techniques for fault diagnosis in manufacturing, emphasizing the importance of selecting appropriate statistical tests like PERMANOVA for hypothesis testing in real-world, fluctuating environments.

Contribution

It develops a hypothesis testing framework specifically for clustering-based fault diagnosis and demonstrates the application of PERMANOVA test in this context.

Findings

PERMANOVA effectively tests hypotheses in clustering fault diagnosis.

Clustering methods outperform supervised learning in fluctuating environments.

The proposed approach addresses real-world data assumptions.

Abstract

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.