Health stages diagnostics of underwater thruster using sound features with imbalanced dataset

Abstract

The underwater thruster is considered one of the most critical components located on an unmanned underwater vehicle to maneuver in the water. However, it is recognized as a common source of the fault. This phenomenon is made worse when collected data for equipment health diagnostics are highly imbalanced. A new sampling method to tackle the problem of imbalanced data based on cosine similarity is proposed to improve the classification accuracy for thruster health diagnostics. The results show that it outperforms SMOTE (Synthetic Minority Oversampling Technique) and ADASYN (Adaptive Synthetic Sampling Approach for Imbalanced Learning). The proposed method was further validated using different imbalanced datasets with different imbalance ratio from KEEL and UCI machine learning repository (such as Pima Indians Diabetes, Ionosphere, Fertility Diagnostics, Mammographic Masses, Blood Transfusion Service Centre). The majority of the results from the datasets show that the proposed method produces the higher classification accuracy as well as g-means that suggests the potential approach for classification problem that has a highly imbalanced dataset.