唐荻音 - 北京航空航天大学 - 自动化科学与电气工程学院

个人简介

本科、博士先后毕业于北京航空航天大学高等工程学院（现高等理工学院）和自动化科学与电气工程学院，2015年博士毕业后入职北京航空航天大学自动化科学与电气工程学院工作至今。长期从事空天装备状态监测、故障诊断、预测与健康管理技术（PHM）的理论研究与应用实践工作。承担了国家自然科学基金（青年、面上）、市级重大技术需求“揭榜挂帅”科技攻关项目、多项企业联合基金等，作为核心技术人员参与了国家重点研发计划等课题。在IEEE trans、《仪器仪表学报》等期刊发表论文20余篇，申请国家发明专利30余项。获中国航空学会科技成果二等奖1项（排名2）。承担学院本科生专业基础课《电路》、研究生基础及学科理论核心课《检测技术与自动化》、研究生实验课《嵌入式自动化装置实验》教学工作，获2022校青年教师教学基本功比赛二等奖。指导学生获北京市优秀本科毕业设计、校级优秀硕士学位论文、北京自动化学会优秀研究生（博士、硕士）等。获校级优秀班主任、生产实习校级优秀教师一等奖、中国仪器仪表学会教学成果奖二等奖（排名3）、校级教学成果一等奖2项。教育经历 2008.9 -- 2015.6 北京航空航天大学自动化科学与电气工程学院控制科学与工程博士研究生毕业博士学位 2012.10 -- 2013.9 多伦多大学机械与工业工程系工业工程联合培养联合培养 2004.9 -- 2008.6 北京航空航天大学高等工程学院（现高等理工学院）自动化大学本科毕业学士学位工作经历 2015.9 -- 至今北京航空航天大学自动化科学与电气工程学院检测与自动化工程系

研究领域

多传感器数据融合状态监测、故障诊断、预测与健康管理技术（PHM）数字孪生PHM/PHM数字工程 PHM领域知识图谱智能运维与维修决策

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

A Digital Twin approach based on nonparametric Bayesian network for complex system health monitoring A power transfer model-based method for lithium-ion battery discharge time prediction of electric rotatory-wing UAV System-Level Performance Prediction for Infrared Systems Based on Energy Redistribution in Infrared Images Telemetry Data-Based Spacecraft Anomaly Detection With Spatial–Temporal Generative Adversarial Networks An Optimal Condition-Based Maintenance Policy for a Degrading System Subject to the Competing Risks of Soft and Hard Failure Multivariate Time Series Anomaly Detection With Generative Adversarial Networks Based on Active Distortion Transformer Health Indicator Construction Based on Multisensors for Intelligent Remaining Useful Life Prediction: A Reinforcement Learning Approach Health Assessment for RUL Prediction of Machinery Components Using Low-Sampling Temporal Signals: A Condensed Image Coding Approach Explaining Anomalous Events in Flight Data of UAV With Deep Attention-Based Multi-Instance Learning Joint optimization of inspection and maintenance strategy for complex multi-component systems using a quantum-inspired genetic algorithm Short-Term Forecasting Based on Graph Convolution Networks and Multiresolution Convolution Neural Networks for Wind Power Health Indicator Construction of High-Speed Rotating Bearings in Aerospace CMG Based on Physics-Inspired Machine Learning Approach An HDP-HMM based approach for tool wear estimation and tool life prediction Online state-of-health prediction of lithium-ion batteries with limited labeled data Multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment Optimizing sequential diagnostic strategy for large-scale engineering systems using a quantum-inspired genetic algorithm: A comparative study An Optimal Burn-In Policy for Cellular Phone Lithium-Ion Batteries Using a Feature Selection Strategy and Relevance Vector Machine Dynamic condition-based maintenance policy for degrading systems described by a random-coefficient autoregressive model: A comparative study Remaining useful life prediction for engineering systems under dynamic operational conditions: A semi-Markov decision process-based approach A prediction method for discharge voltage of lithium-ion batteries under unknown dynamic loads Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction Remaining Discharge Time Prognostics of Lithium-Ion Batteries Using Dirichlet Process Mixture Model and Particle Filtering Method Remaining useful life prediction for lithium-ion batteries using a quantum particle swarm optimization-based particle filter Remaining useful life estimation for deteriorating systems with time-varying operational conditions and condition-specific failure zones Optimal replacement policy for a periodically inspected system subject to the competing soft and sudden failures Optimal maintenance policy and residual life estimation for a slowly degrading system subject to condition monitoring A Novel PF-LSSVR-based Framework for Failure Prognosis of Nonlinear Systems with Time-varying Parameters Remaining useful life prognostic estimation for aircraft subsystems or components: A review