An oversampling method based on Gaussian Mixture Model for multi-bolt looseness monitoring using Lamb waves`

The imbalance in the number of healthy and faulty samples poses a significant hindrance to the successful implementation of multi-bolt looseness monitoring and fault classification in engineering applications. The Synthetic Minority Oversampling Technique (SMOTE) is widely used in addressing data imbalance issues. However, traditional SMOTE methods and their improvements have not taken into account the problem of sensor network signals with multiple paths. In this paper, an improved oversampling technique, namely Gaussian Mixture Model (GMM)-SMOTE, is introduced. The proposed SMOTE method is based on GMM and aims to solve the sample imbalance problem under different bolt states and different sensor paths in sensor networks. The GMM is utilized to cluster the minority classes in multi-bolt looseness data. The assignment of weights to different clusters is based on the clustering density function, which is subsequently followed by oversampling. The KL distance is utilized to screen the SMOTE sample data, which improves the quality of the data by achieving inter-class balance and intra-class balance. To verify the effectiveness of the method, a multi-bolt looseness monitoring procedure is implemented. The experimental results demonstrate that the proposed method can improve the accuracy of diagnosing early-stage bolt looseness.