Brief

Researchers have developed a novel method for predicting the Remaining Useful Life (RUL) of industrial equipment by leveraging pre-trained time-series foundation models (TSFMs). This approach uses Chronos-2 as a frozen backbone to extract features, which are then fed into a lightweight regression neural network for RUL estimation. Experiments on real-world data demonstrate that this method significantly outperforms traditional baselines, offering a more data-efficient and practical solution for industrial predictive maintenance. AI

Researchers have developed APEX, a new network-native transformer model designed for time-series forecasting and anomaly detection in wireless network operations. Unlike generic models, APEX is specifically pre-trained on telemetry data from thousands of wireless networks, enabling it to better handle the unique characteristics of this data. The model, available in both large and edge versions, significantly outperforms existing baselines in predicting network degradations and identifying anomalies, with the edge version offering efficient on-device inference. AI