Review of PHM Data Competitions from 2008 to 2017
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Bin Huang | Jay Lee | Jianshe Feng | Haoshu Cai | Xiaodong Jia | J. Lee | Xiaodong Jia | Jianshe Feng | Bin Huang | Haoshu Cai
[1] Tianyi Wang,et al. Trajectory Similarity Based Prediction for Remaining Useful Life Estimation , 2010 .
[2] Yaguo Lei,et al. An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings , 2015, IEEE Transactions on Industrial Electronics.
[3] M. Pecht,et al. Estimation of remaining useful life of ball bearings using data driven methodologies , 2012, 2012 IEEE Conference on Prognostics and Health Management.
[4] Huimin Chen. AMultiple Model Prediction Algorithm for CNC Machine Wear PHM , 2011 .
[5] L. Peel,et al. Data driven prognostics using a Kalman filter ensemble of neural network models , 2008, 2008 International Conference on Prognostics and Health Management.
[6] David Siegel,et al. Prognostics and Health Assessment of a Multi-Regime System using a Residual Clustering Health Monitoring Approach , 2013 .
[7] Sunuwe Kim,et al. Fault Log Recovery Using an Incomplete-data-trained FDA Classifier for Failure Diagnosis of Engineered Systems , 2020 .
[8] David Siegel,et al. A Systematic Methodology for Gearbox Health Assessment and Fault Classification , 2009 .
[9] Santanu Das,et al. Maintenance Action Recommendation Using Collaborative Filtering , 2020, International Journal of Prognostics and Health Management.
[10] Fan-Tien Cheng,et al. Run-to-Run Control Utilizing Virtual Metrology With Reliance Index , 2013, IEEE Transactions on Semiconductor Manufacturing.
[11] F.O. Heimes,et al. Recurrent neural networks for remaining useful life estimation , 2008, 2008 International Conference on Prognostics and Health Management.
[12] Liliane Pintelon,et al. Prognostic maintenance scheduling for offshore wind turbine farms , 2012 .
[13] Wei-Ping Xiao,et al. A Probabilistic Machine Learning Approach to Detect Industrial Plant Faults , 2016, International Journal of Prognostics and Health Management.
[14] Lockheed Martin,et al. Essential steps in prognostic health management , 2011, 2011 IEEE Conference on Prognostics and Health Management.
[15] Jay Lee,et al. An Auto-Associative Residual Processing and K-means Clustering Approach for Anemometer Health Assessment , 2011 .
[16] T. Nakagawa. Periodic and sequential preventive maintenance policies , 1986 .
[17] Wenjing Jin,et al. Enhanced Restricted Boltzmann Machine With Prognosability Regularization for Prognostics and Health Assessment , 2016, IEEE Transactions on Industrial Electronics.
[18] Jay Lee,et al. An Effective Predictive Maintenance Approach based on Historical Maintenance Data using a Probabilistic Risk Assessment: PHM14 Data Challenge , 2020 .
[19] Longji Sun,et al. Feature Extraction and Pattern Identification for Anemometer Condition Diagnosis , 2012 .
[20] Byeng D. Youn,et al. Risk Prediction of Engineering Assets: An Ensemble of Part Lifespan Calculation and Usage Classification Methods , 2020 .
[21] Jay Lee,et al. Information Reconstruction Method for Improved Clustering and Diagnosis of Generic Gearbox Signals , 2011 .
[22] Michael Pecht,et al. Prognostics uncertainty reduction by fusing on-line monitoring data based on a state-space-based degradation model , 2014 .
[23] Jorge F. Silva,et al. Particle-Filtering-Based Prognosis Framework for Energy Storage Devices With a Statistical Characterization of State-of-Health Regeneration Phenomena , 2013, IEEE Transactions on Instrumentation and Measurement.
[24] Jay Lee,et al. Enhanced Virtual Metrology on Chemical Mechanical Planarization Process using an Integrated Model and Data-Driven Approach , 2020 .
[25] Pavle Boskoski,et al. Bearing fault prognostics based on signal complexity and Gaussian process models , 2012, 2012 IEEE Conference on Prognostics and Health Management.
[26] Yuan Di,et al. Adaptive virtual metrology for semiconductor chemical mechanical planarization process using GMDH-type polynomial neural networks , 2018 .
[27] Yan Chen,et al. Data Quality Assessment Methodology for Improved Prognostics Modeling , 2012 .
[28] Yixiang Huang,et al. Feature Extraction and Ensemble Decision Tree Classifier in Plant Failure Detection , 2015 .
[29] Michael Pecht,et al. Application of a state space modeling technique to system prognostics based on a health index for condition-based maintenance , 2012 .
[30] Jay Lee,et al. Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .
[31] James Kuria Kimotho,et al. Application of Event Based Decision Tree and Ensemble of Data Driven Methods for Maintenance Action Recommendation , 2013 .
[32] Feibai Zhu,et al. Data quality evaluation and improvement for prognostic modeling using visual assessment based data partitioning method , 2013, Comput. Ind..
[33] Walter Sextro,et al. PEM fuel cell prognostics using particle filter with model parameter adaptation , 2014, 2014 International Conference on Prognostics and Health Management.
[34] Xiaodong Jia,et al. Prognosability study of ball screw degradation using systematic methodology , 2018, Mechanical Systems and Signal Processing.
[35] Jianbo Yu,et al. A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems , 2008, 2008 International Conference on Prognostics and Health Management.
[36] Jay Lee,et al. A deviation based assessment methodology for multiple machine health patterns classification and fault detection , 2018 .
[37] Jay Lee,et al. Assessment of Data Suitability for Machine Prognosis Using Maximum Mean Discrepancy , 2018, IEEE Transactions on Industrial Electronics.
[38] Christos Emmanouilidis,et al. A Bayesian Approach for Maintenance Action Recommendation , 2013 .
[39] Wlamir Olivares Loesch Vianna,et al. Proton Exchange Membrane Fuel Cells (PEMFC) impedance estimation using regression analysis , 2014, 2014 International Conference on Prognostics and Health Management.