With emerging smart communities, improving overall system availability is becoming a major concern. In order to improve the reliability of the components in a system we propose an inference model to predict Remaining Useful Life (RUL) of those components. In this paper we work with components of backend data servers such as hard disks, that are subject to degradation. A Deep Long-Short Term Memory (LSTM) Network is used as the backbone of this fast, data-driven decision framework and dynamically captures the pattern of the incoming data. In the article, we discuss the architecture of the neural network and describe the mechanisms to choose the various hyper-parameters. Further, we describe the challenges faced in extracting effective training sets from highly unorganized and class-imbalanced big data and establish methods for online predictions with extensive data pre-processing, feature extraction and validation through online simulation sets with unknown remaining useful lives of the hard disks. Our algorithm performs especially well in predicting RUL near the critical zone of a device approaching failure. With the proposed approach we are able to predict whether a disk is going to fail in next ten days with an average precision of 0.8435. We also show that the architecture trained on a particular model can be used to predict RUL for devices in different models from same manufacturer through transfer learning.
HyBattNet: Hybrid Framework for Predicting the Remaining Useful Life of Lithium-Ion Batteries
AIWARE Limited Company（AIWARE有限公司）；Faculty of Information Technology, Ton Duc Thang University（信息科技学院，-ton Duc Thang大学）；Software Engineering Department, FPT University（软件工程系，FPT大学）；Laboratory for Applied and Industrial Mathematics, Institute for Computational Science and Artificial Intelligence, Van Lang University（应用与工业数学实验室，计算科学与人工智能研究所，Van Lang大学）
Bi-cLSTM: Residual-Corrected Bidirectional LSTM for Aero-Engine RUL Estimation
Department of Computer Science and Engineering（计算机科学与工程系）；Rajshahi University of Engineering and Technology（拉贾沙希工程与技术大学）；Department of Aeronautical Engineering（航空工程系）；Military Institute of Science and Technology（军事科学与技术学院）
LSTM-Based Forecasting and Analysis of EV Charging Demand in a Dense Urban Campus
Electrical and Electronic Engineering Department, California State University-Sacramento（电气与电子工程系，加州州立大学萨克拉门托分校）；Electrical and Electronic Engineering Department, University of California, Riverside（电气与电子工程系，加州大学河滨分校）；Computer Science Department, California State University, Northridge（计算机科学系，加州州立大学北岭分校）；Computer Engineering Department, California State University, Northridge（计算机工程系，加州州立大学北岭分校）
本文提出一种结合插值、归一化与特征提取的 LSTM 充电负荷预测框架，可在日、周、月尺度上准确预测多地点 EV 充电需求，并支持不同部署场景。
Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation
Singapore University of Technology and Design（新加坡科技设计大学）；Chongqing University（重庆大学）；Inner Mongolia University of Science and Technology（内蒙古科技大学）；Institute for Infocomm Research (I2R), A*STAR（新加坡科技研究局信息通信研究所（I2R））
A Multi-Task Targeted Learning Framework for Lithium-Ion Battery State-of-Health and Remaining Useful Life
School of Electronics and Information Engineering, Hangzhou Dianzi University（杭州电子科技大学电子与信息工程学院）；School of Information Technology and Artificial Intelligence, Zhejiang University of Finance and Economics（浙江财经大学信息科技与人工智能学院）；Engineering Training Center, China Jiliang University（中国浙江科技大学工程训练中心）
Robust-MBDL: A Robust Multi-branch Deep Learning Based Model for Remaining Useful Life Prediction and Operational Condition Identification of Rotating Machines
University of Science and Technology - The University of Danang（岘港大学科技大学）；Institute for Computational Science and Artificial Intelligence, Van Lang University（文郎大学计算科学与人工智能研究院）；Van Lang University（文郎大学）；AIT Austrian Institute of Technology GmbH（奥地利AIT技术研究院有限公司）；University of Technology of Compiègne（贡比涅技术大学）
Remaining useful life prediction of Lithium-ion batteries using spatio-temporal multimodal attention networks
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；RPTU Kaiserslautern-Landau（凯泽斯劳滕-兰道工业大学）；University of Lorraine（洛林大学）；Centre de Recherche en Automatique de Nancy (CRAN)（南锡自动化研究中心）
Comparative Analysis of Mel-Frequency Cepstral Coefficients and Wavelet Based Audio Signal Processing for Emotion Detection and Mental Health Assessment in Spoken Speech
Leakage-Robust Evaluation and Data-Scale Sensitivity of Attention-Enhanced Multi-Task Learning for Joint Fault Diagnosis and Remaining Useful Life Estimation
Western Illinois University（西伊利诺伊大学）；Ahsanullah University of Science and Technology（阿山努拉科技大学）；Trine University（特莱恩大学）；Sapienza University of Rome（罗马第一大学）；Bangladesh University of Engineering and Technology（孟加拉工程技术大学）；Chittagong University of Engineering and Technology（吉大港工程技术大学）；Rajshahi University of Engineering & Technology (RUET)（拉杰沙希工程技术大学）
