Deep neural networks (DNNs) are widely used for fault classification using partial discharges (PDs) to evaluate various electrical apparatuses and achieve high classification accuracy pertaining to trained PD faults. However, there is a risk of false alarm in the case of untrained PD faults because it is difficult for DNNs to predict data that were not included in the training process. In this paper, we research classification problems of unknown classes using PDs in gas-insulated switchgears (GISs) and propose a deep ensemble model to obtain the confidence of output probability and determine thresholds to detect unknown fault classes. The proposed model was verified by real-world phase-resolved PD (PRPD) experiments using online ultra-high frequency (UHF) PD measurement systems. The experimental results show that the proposed model achieves better unknown detection performance for the untrained PD faults and retains the classification performance for the trained PD faults.

Detecting, measuring, and classifying partial discharges (PDs) are important tasks for assessing the condition of insulation systems used in different electrical equipment. Owing to the implementation of the phase-resolved PD (PRPD) as a sequence input, an existing method that processes sequential data, e.g., the recurrent neural network, using a long short-term memory (LSTM) has been applied for fault classification. However, the model performance is not further improved because of the lack of supporting parallel computation and the inability to recognize the relevance of all inputs. To overcome these two drawbacks, we propose a novel deep-learning model in this study based on a self-attention mechanism to classify the PD patterns in a gas-insulated switchgear (GIS). The proposed model uses a self-attention block that offers the advantages of simultaneous computation and selective focusing on parts of the PRPD signals and a classification block to finally classify faults in the GIS. Moreover, the combination of LSTM and self-attention is considered for comparison purposes. The experimental results show that the proposed method achieves performance superiority compared with the previous neural networks, whereas the model complexity is significantly reduced.

In recent years, deep learning has been successfully used in order to classify partial discharges (PDs) for assessing the condition of insulation systems in different electrical equipment. However, fault diagnosis using deep learning is still challenging, as it requires a large amount of training data, which is difficult and expensive to obtain in the real world. This paper proposes a novel one-shot learning method for fault diagnosis using a small dataset of phase-resolved PDs (PRPDs) in a gas-insulated switchgear (GIS). The proposed method is based on a Siamese network framework, which employs a distance metric function for predicting sample pairs from the same PRPD class or different PRPD classes. Experimental results over the small PRPD dataset that was obtained from an ultra-high-frequency sensor in the GIS show that the proposed method achieves outstanding performance for PRPD fault diagnosis as compared with the previous methods.

Gearbox condition monitoring plays a crucial role in ensuring the reliability and safety of mechanical transmission systems in road vehicles. This study proposes an XGBoost-based fault diagnosis method using time-domain signals collected from four wheels—front-left, front-right, rear-left, and rear-right—under real-world operational conditions. Twelve statistical features extracted from the wheel-speed signals, combined with road vehicle characteristics, are considered as input for the model. The performance of the proposed method is verified through time-domain experiments. The experimental results indicate that the proposed XGBoost approach achieves superior fault classification accuracy compared to traditional tree-based ensemble methods such as Decision Trees and Random Forests, at 82.42%, 75.82%, and 72.53%, respectively. The method offers an effective tool for real-time gearbox fault diagnosis in complex vehicle environments.