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1. Full Text Bayesian Reliability-Based Design Optimization Using Eigenvector Dimension Reduction (Edr) Method
by Wang, Pingfeng, author
SAE technical paper series, Volume 2007-01-0559.
Design processes | Reliability
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2. Full Text Failure diagnosis using deep belief learning based health state classification
by Tamilselvan, Prasanna and Wang, Pingfeng
Reliability engineering & system safety, ISSN 0951-8320, 07/2013, Volume 115, pp. 124 - 135
Fault diagnosis | Deep belief networks | Artificial intelligence in diagnosis Classification | Operations Research & Management Science | Engineering | Engineering, Industrial | Technology | Science & Technology | Reliability theory. Replacement problems | Learning and adaptive systems | Operational research and scientific management | Exact sciences and technology | Applied sciences | Connectionism. Neural networks | Artificial intelligence | Operational research. Management science | Logistics | Computer science; control theory; systems | Neural networks | Analysis | Learning | Health | Classification | Inference | Diagnosis | Safety | Failure | Belief networks
Journal Article
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3. Engineering resilience quantification and system design implications: A literature survey
by Yodo, Nita and Wang, Pingfeng
Journal of mechanical design (1990), ISSN 1050-0472, 11/2016, Volume 138, Issue 11
Engineering | Technology | Engineering, Mechanical | Science & Technology
Journal Article
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4. Full Text A control-guided failure restoration framework for the design of resilient engineering systems
by Yodo, Nita and Wang, Pingfeng
Reliability engineering & system safety, ISSN 0951-8320, 10/2018, Volume 178, pp. 179 - 190
Engineering design | Implicit systems | Control theory | Reliability | Failure restoration | Resilience | Operations Research & Management Science | Engineering | Engineering, Industrial | Technology | Science & Technology | Case studies | Neural networks | Mechanical engineering | Analysis | Systems stability | Design parameters | Feasibility studies | Restoration strategies | Artificial neural networks | Dynamical systems | Design engineering | Failure analysis | Modelling | Mathematical models
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5. A maximum confidence enhancement based sequential sampling scheme for simulation-based design
by Wang, Zequn and Wang, Pingfeng
Journal of mechanical design (1990), ISSN 1050-0472, 02/2014, Volume 136, Issue 2, pp. np - np
Engineering | Technology | Engineering, Mechanical | Science & Technology | Confidence intervals | Design engineering | Sequential sampling | Computer simulation | Confidence | Mathematical models | Robustness | Computational efficiency
Journal Article
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6. A nested extreme response surface approach for time-dependent reliability-based design optimization
by Wang, Zequn and Wang, Pingfeng
Journal of mechanical design (1990), ISSN 1050-0472, 2012, Volume 134, Issue 12
Engineering | Technology | Engineering, Mechanical | Science & Technology
Journal Article
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7. Full Text A double-loop adaptive sampling approach for sensitivity-free dynamic reliability analysis
by Wang, Zequn and Wang, Pingfeng
Reliability engineering & system safety, ISSN 0951-8320, 10/2015, Volume 142, pp. 346 - 356
Reliability analysis | Dynamic | Surrogate model | Sequential sampling | Sensitivity free | Operations Research & Management Science | Engineering | Engineering, Industrial | Technology | Science & Technology | Case studies | Monte Carlo method | Analysis | Dynamic tests | Computer simulation | Adaptive sampling | Dynamics | Computational efficiency | Sampling | Dynamical systems
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8. Resilience Allocation for Early Stage Design of Complex Engineered Systems
by Yodo, Nita and Wang, Pingfeng
Journal of mechanical design (1990), ISSN 1050-0472, 09/2016, Volume 138, Issue 9
Engineering | Technology | Engineering, Mechanical | Science & Technology
Journal Article
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9. Resilience Modeling and Quantification for Engineered Systems Using Bayesian Networks
by Yodo, Nita and Wang, Pingfeng
Journal of mechanical design (1990), ISSN 1050-0472, 03/2016, Volume 138, Issue 3
Engineering | Technology | Engineering, Mechanical | Science & Technology | Design engineering | Supply chains | Failure analysis | Modelling | Disruption | Assessments | Bayesian analysis | Resilience
Journal Article
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10. Full Text Engineering Design under Uncertainty and Health Prognostics
by Hu, Chao and Youn, Byeng D and Wang, Pingfeng
2018, Springer Series in Reliability Engineering, ISBN 9783319925721, 350
Engineering design | Reliability (Engineering)
eBook
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11. Full Text A generic model-free approach for lithium-ion battery health management
by Bai, Guangxing and Wang, Pingfeng and Hu, Chao and Pecht, Michael
Applied energy, ISSN 0306-2619, 12/2014, Volume 135, pp. 247 - 260
State-of-charge | Health management | State-of-health | Artificial neural network | Kalman filter | Battery | Engineering | Engineering, Chemical | Technology | Energy & Fuels | Science & Technology | Electrical engineering. Electrical power engineering | Energy | Electrical power engineering | Electrochemical conversion: primary and secondary batteries, fuel cells | Exact sciences and technology | Applied sciences | Direct energy conversion and energy accumulation | Analysis | Batteries | Battery industry | Models | Neural networks
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12. An Integrated Performance Measure Approach for System Reliability Analysis
by Wang, Zequn and Wang, Pingfeng
Journal of mechanical design (1990), ISSN 1050-0472, 02/2015, Volume 137, Issue 2, pp. np - np
Engineering | Technology | Engineering, Mechanical | Science & Technology | Monte Carlo methods | Accuracy | Approximation | Computer simulation | Mathematical analysis | Mathematical models | System reliability | Assessments | Failure
Journal Article
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13. Full Text An internal state variable mapping approach for Li-Plating diagnosis
by Bai, Guangxing and Wang, Pingfeng
Journal of power sources, ISSN 0378-7753, 08/2016, Volume 323, pp. 115 - 124
Kriging model | Li-plating | Electrochemical model | Li-ion battery | COMSOL multiphysics | Physical Sciences | Chemistry | Materials Science | Technology | Materials Science, Multidisciplinary | Electrochemistry | Chemistry, Physical | Energy & Fuels | Science & Technology | Case studies | Analysis | Batteries | Neural networks | Fading | Mapping | Diagnosis | Electric batteries | Dynamical systems | Failure | Rechargeable batteries | State variable
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14. Full Text A generic probabilistic framework for structural health prognostics and uncertainty management
by Wang, Pingfeng and Youn, Byeng D and Hu, Chao
Mechanical systems and signal processing, ISSN 0888-3270, 04/2012, Volume 28, pp. 622 - 637
Health prognostics | Sparse bayes learning | Similarity | Uncertainty management | Remaining useful life | Synthesized health index | Engineering | Technology | Engineering, Mechanical | Science & Technology | Learning and adaptive systems | Mechanical engineering. Machine design | Exact sciences and technology | Applied sciences | General | Artificial intelligence | Computer science; control theory; systems | Industrial metrology. Testing | Generic drugs | Mechanical engineering | Aerospace engineering | Learning | Interpolation | Uncertainty | Health | On-line systems | Management | Mechanical systems | Real time
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15. Full Text Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life
by Hu, Chao and Youn, Byeng D and Wang, Pingfeng and Taek Yoon, Joung
Reliability engineering & system safety, ISSN 0951-8320, 07/2012, Volume 103, pp. 120 - 135
RUL prediction | Weighting schemes | k-fold cross validation | Data-driven prognostics | Ensemble | Operations Research & Management Science | Engineering | Engineering, Industrial | Technology | Science & Technology | Reliability theory. Replacement problems | Energy | Operational research and scientific management | Exact sciences and technology | Installations for energy generation and conversion: thermal and electrical energy | Applied sciences | Energy. Thermal use of fuels | Operational research. Management science | Fission nuclear power plants | Nuclear energy | Nuclear power plants | Algorithms | Mechanical engineering | Aerospace engineering
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