Remaining useful life (RUL) is a metric of health state for essential equipment. It plays a significant role in health management. However, RUL is often random and unknown. One type of physics-based method builds a mathematical model for RUL using prior principles, but this is a tough task in real-world applications. Another type of method estimates RUL from available information through condition and health monitoring; this is known as the data-driven method. Traditional data-driven methods require significant human effort in designing health features to represent performance degradation, yet the prediction accuracy is limited. With breakthroughs in various application scenarios in recent years, deep learning techniques provide new insights into this problem. Over the past few years, deep-learning-based RUL prediction has attracted increasing attention from the academic community. Therefore, it is necessary to conduct a survey on deep-learning-based RUL prediction. To ensure a comprehensive survey, the literature is reviewed from three dimensions. Firstly, a unified framework is proposed for deep-learning-based RUL prediction and the models and approaches in the literature are reviewed under this framework. Secondly, detailed estimation processes are compared from the perspective of different deep learning models. Thirdly, the literature is examined from the perspective of specific problems, such as scenarios where the collected data consist of limited labeled data. Finally, the main challenges and future directions are summarized.

Network interconnection critically impacts the performance of data centers (DCs) and high-performance computing (HPC) systems, with scalability becoming vital as computing demands grow. This necessitates interconnection architectures that meet stringent latency, bandwidth, cost, and power consumption requirements. Optical interconnections provide cost-efficiency, reduced power consumption, and scalability to fulfill bandwidth needs. However, optical switches lack optical buffers, complicating the operation of all optical networks. To this end, we propose HiveNet , a novel hybrid interconnect architecture based on dual-port nodes and arrayed waveguide grating routers (AWGRs). HiveNet integrates low-radix electrical switches at lower layers to reduce cable complexity and construction costs, while AWGR-based optical connections at upper layers ensure fast switching and high bandwidth. The dual-port capability enables robust fault tolerance and supports all communication types (node-switch and node-node). Furthermore, a customized routing algorithm significantly enhances performance. Simulations conducted under various traffic patterns demonstrate that HiveNet achieves controlled delay and superior aggregate throughput. For large-scale networks (with 104,976 nodes at 10 Gb/s), HiveNet reduces construction costs by 49.3%, 26.4%, 32.7%, 54.1%, and 59.3% compared to Fat - Tree , H - LION , Leaf - Spine , BCube , and Lotus , respectively. Additionally, HiveNet decreases power consumption by 34.8%, 48.2%, 29.8%, and 23.1% compared to Fat - Tree , BCube , Leaf - Spine , and Lotus , respectively.