We focus on Quality Control and Reliability Engineering of intelligent manufacturing and complex systems through engineering-informed machine learning and data analytics.
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Data analytics for manufacturing system improvement: metal matrix nanocomposites fabrication; 3D printing, steel manufacturing; quality characterization and quantification; nondestructive inspection; modeling, monitoring and analysis for quality control and process improvement.
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Data-driven fault detection, diagnostics and prognostics of complex systems: integration of machine learning, statistical modeling and reliability theory for anomaly detection, diagnostics and prognostics of smart and connected systems, e.g., aero-engines.
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Data analytics methodology development: deep learning; online change-point detection; steady-state detection; Bayesian inference; state-space models; sequential Monte Carlo techniques; high-dimensional data fusion and monitoring; Gaussian process.
基于工程机器学习（engineering-informed ML）与大数据分析的智能制造与复杂系统质量与可靠性研究—融合工程知识与机器学习、数据分析技术，对智能制造过程与复杂系统进行智能监测、诊断、预测以及优化，以提升其质量和可靠性。
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先进制造系统质量控制与优化：超强纳米复合材料制备、增材制造、特钢制造等过程建模与分析、在线异常检测与诊断、过程优化与控制
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高端装备、系统故障诊断与预测：面向航空发动机、电池、智能网联系统的建模、监测、故障诊断、寿命预测
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面向智能系统的机器学习/统计学习方法研究：深度学习、大数据融合技术、变点检测、稳态检测、高维时序数据建模、融合与监测、贝叶斯推理、粒子滤波
We are looking for excellent and self-motivated PhD students every year. 欢迎成绩优异的本科、硕士生加入我们实验室攻读博士学位！
Lab Director: Prof. Jianguo Wu （吴建国博士）
Contact（联系方式）:
Prof. Jianguo Wu
Department of Industrial Engineering and Management
College of Engineering, Peking University
Room 901, Wang Kezhen Building (王克桢楼901室)
52 Haidian Road, Beijing, China 100080
Phone: 010-82529021
Email: j.wu[AT]pku.edu.cn
