Accurate estimation of remaining useful life (RUL) of industrial equipment can enable advanced maintenance schedules, increase equipment availability and reduce operational costs. However, existing deep learning methods for RUL prediction are not completely successful due to the following two reasons. First, relying on a single objective function to estimate the RUL will limit the learned representations and thus affect the prediction accuracy. Second, while longer sequences are more informative for modelling the sensor dynamics of equipment, existing methods are less effective to deal with very long sequences, as they mainly focus on the latest information. To address these two problems, we develop a novel attention-based sequence to sequence with auxiliary task (ATS2S) model. In particular, our model jointly optimizes both reconstruction loss to empower our model with predictive capabilities (by predicting next input sequence given current input sequence) and RUL prediction loss to minimize the difference between the predicted RUL and actual RUL. Furthermore, to better handle longer sequence, we employ the attention mechanism to focus on all the important input information during training process. Finally, we propose a new dual-latent feature representation to integrate the encoder features and decoder hidden states, to capture rich semantic information in data. We conduct extensive experiments on four real datasets to evaluate the efficacy of the proposed method. Experimental results show that our proposed method can achieve superior performance over 13 state-of-the-art methods consistently.
An empirical evaluation of attention-based multi-head models for improved turbofan engine remaining useful life prediction
Zhejiang University（浙江大学）；Tianjin Jinhang Technical Physics Institute（天津津航技术物理研究所）；Complex System Control and Intelligent Agent Cooperative Laboratory（复杂系统控制与智能体协同实验室）
Remaining Useful Life Prediction for Batteries Utilizing an Explainable AI Approach with a Predictive Application for Decision-Making
Nepal Engineering College, Pokhara University（博卡拉大学尼泊尔工程学院）；University of Delaware（特拉华大学）；Delft University of Technology（代尔夫特理工大学）；The Hague University of Applied Science（海牙应用科学大学）；Institute of Engineering, Pulchowk Campus, Tribhuvan University（特里布文大学工程学院普尔乔克校区）
A temporal scale transformer framework for precise remaining useful life prediction in fuel cells
University College London（伦敦大学学院）；University of Sheffield（谢菲尔德大学）；University of Liverpool（利物浦大学）；Newcastle University（纽卡斯尔大学）；Iowa State University（爱荷华州立大学）
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）
Federated learning framework for collaborative remaining useful life prognostics: an aircraft engine case study
Department of Information and Computing Sciences, Utrecht University（乌特勒支大学信息与计算科学系）；Faculty of Aerospace Engineering, Delft University of Technology（代尔夫特理工大学航空航天工程学院）
Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery
Singapore University of Technology and Design（新加坡科技设计大学）；University of Moratuwa（莫拉图瓦大学）；University of Melbourne（墨尔本大学）；Keysight Technologies（是德科技）；National University of Singapore（新加坡国立大学）
Interaction models for remaining useful life estimation
Artificial Intelligence Research Institute (AIRI)（人工智能研究院（AIRI））；JCS MERI；HSE University（高等经济大学）；National University of Science and Technology MISIS（国立科技研究大学MISIS）
Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics
Two-stage Early Prediction Framework of Remaining Useful Life for Lithium-ion Batteries
RPTU Kaiserslautern-Landau（凯泽斯劳滕-兰道莱茵兰-普法尔茨工业大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；University of Lorraine（洛林大学）
Pre-Trained Large Language Model Based Remaining Useful Life Transfer Prediction of Bearing
Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；Institute of Reliability Engineering, Beihang University（北京航空航天大学可靠性工程研究所）；School of Reliability and Systems Engineering, Beihang University（北京航空航天大学可靠性与系统工程学院）；China Electronics Information Industry Group Co., Ltd. Sixth Research Institute（中国电子信息产业集团有限公司第六研究所）
Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
Concordia University（康考迪亚大学）；Gina Cody School of Engineering and Computer Science（吉娜·科迪工程与计算机科学学院）；Università degli Studi di Padova（帕多瓦大学）；Mila-Quebec AI Institute（米拉-魁北克人工智能研究所）
Quantum Annealing Enhanced Reinforcement Learning for Accurate Remaining Useful Lifetime Prediction
Central University of Karnataka（卡纳塔克中央大学）；University College of Engineering, Anna University（安娜大学工程学院）；AIONOS India Pvt Ltd（AIONOS印度私人有限公司）；National Institute of Technology Tiruchirappalli（蒂鲁吉拉帕利国立理工学院）
A deep learning-based remaining useful life prediction approach for bearings
Huazhong University of Science and Technology（华中科技大学）；Wuhan University of Science and Technology（武汉科技大学）；Zhejiang University（浙江大学）；Imperial College London（帝国理工学院）
BACE-RUL: A Bi-directional Adversarial Network with Covariate Encoding for Machine Remaining Useful Life Prediction
School of Software Engineering, Sun Yat-Sen University（中山大学软件学院）；School of Computer Science and Technology, China University of Mining and Technology（中国矿业大学计算机科学与技术学院）；National University of Singapore（新加坡国立大学）
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）
