Fast feature selection based on cluster validity index applied on data-driven bearing fault detection

The Prognostics and Health Management (PHM) approach aims to reduce potential failures or machine downtime by determining the system state through the identification of the signals changes produced by the system's faults. Machine learning (ML) approaches for fault diagnosis usually have high-dimensional feature space that can be obtained from signal processing. Nevertheless, as more features are included in the ML algorithms the processing time increases, there is a tendency for overfitting, and the performance may even decrease. Feature selection has multiple goals including building more simple and comprehensible models, improving the performance on ML algorithms, and preparing clean and understandable data. This paper proposes a methodological framework based on a cluster validity index (CVI) and Sequential Forward Search (SFS) to select the best subset of features applied on the problem of fault severity classification in rolling bearing. The results show that a perfect classification can be obtained with KNN with at least six selected features.