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Using Clustering techniques and Classification Mechanisms for Fault Diagnosis

In this paper we aim to provide a simulation of processing a real-life fault diagnosis system using a s-set benchmark dataset. In this proposal, firstly we determine the optimal number of clusters using three major techniques. Secondly, we perform clustering to build a comparative study of seven algorithms using evaluation metrics, time taken, shape and density of clusters and effectiveness in detecting the outliers. We select the best clustering algorithm to segment the dataset into the corresponding fault-based groups and label the dataset to be used for classification. Lastly, an automation tool called the TPOT Classifier is applied to find the best classification pipeline that yields a high accuracy. The results obtained are promising and outlay a detailed comparison of the clustering algorithms and classification mechanisms. This methodology can be employed on a real-time sensor data to carry out fault diagnosis effectively.