A Supervised Embedding and Clustering Anomaly Detection method for classification of Mobile Network Faults

The paper introduces Supervised Embedding and Clustering Anomaly Detection (SEMC-AD), a method designed to efficiently identify faulty alarm logs in a mobile network and alleviate the challenges of manual monitoring caused by the growing volume of alarm logs. SEMC-AD employs a supervised embedding approach based on deep neural networks, utilizing historical alarm logs and their labels to extract numerical representations for each log, effectively addressing the issue of imbalanced classification due to a small proportion of anomalies in the dataset without employing one-hot encoding. The robustness of the embedding is evaluated by plotting the two most significant principle components of the embedded alarm logs, revealing that anomalies form distinct clusters with similar embeddings. Multivariate normal Gaussian clustering is then applied to these components, identifying clusters with a high ratio of anomalies to normal alarms (above 90%) and labeling them as the anomaly group. To classify new alarm logs, we check if their embedded vectors' two most significant principle components fall within the anomaly-labeled clusters. If so, the log is classified as an anomaly. Performance evaluation demonstrates that SEMC-AD outperforms conventional random forest and gradient boosting methods without embedding. SEMC-AD achieves 99% anomaly detection, whereas random forest and XGBoost only detect 86% and 81% of anomalies, respectively. While supervised classification methods may excel in labeled datasets, the results demonstrate that SEMC-AD is more efficient in classifying anomalies in datasets with numerous categorical features, significantly enhancing anomaly detection, reducing operator burden, and improving network maintenance.

Detection of Transformer Winding Axial Displacement by Kirchhoff and Delay and sum Radar Imaging Algorithms

In this paper, a novel method for in detail detection of the winding axial displacement in power transformers based on UWB imaging is presented. In this method, the radar imaging process is implemented on the power transformer by using an ultra-wide band (UWB) transceiver. The result is a 2-D image of the transformer winding, which is analyzed to determine the occurrence as well as the magnitude of the winding axial displacement. The method is implemented on a transformer model. The experimental results illustrate the effectiveness of the proposed method.