Health State Classification of a Spherical Tank Using a Hybrid Bag of Features and k-Nearest Neighbor

Feature analysis plays an important role in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designed for the health state classification of spherical tanks in this paper. The proposed HBoF is composed of (a) the acoustic emission (AE) features, and (b) the time and frequency based statistical features. A wrapper-based feature selector algorithm, Boruta, is applied to extract the most intrinsic feature set from HBoF. The selective feature matrix is passed to the k-nearest neighbor (k-NN) classifier to distinguish between normal condition (NC) and faulty condition (FC). Experimental results show that the proposed approach yields an average 100% accuracy for all working conditions. The proposed method outperforms the existing state-of-the-art approaches by achieving at least 19% higher classification accuracy.