David Siegel
David Siegel
Center for Intelligent Maintenance Systems
Verified email at mail.uc.edu
Cited by
Cited by
Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications
J Lee, F Wu, W Zhao, M Ghaffari, L Liao, D Siegel
Mechanical systems and signal processing 42 (1-2), 314-334, 2014
A similarity-based prognostics approach for remaining useful life estimation of engineered systems
T Wang, J Yu, D Siegel, J Lee
2008 international conference on prognostics and health management, 1-6, 2008
Wind turbine performance assessment using multi-regime modeling approach
E Lapira, D Brisset, HD Ardakani, D Siegel, J Lee
Renewable Energy 45, 86-95, 2012
Methodology and framework for predicting helicopter rolling element bearing failure
D Siegel, C Ly, J Lee
IEEE Transactions on Reliability 61 (4), 846-857, 2012
Novel method for rolling element bearing health assessment—A tachometer-less synchronously averaged envelope feature extraction technique
D Siegel, H Al-Atat, V Shauche, L Liao, J Snyder, J Lee
Mechanical Systems and Signal Processing 29, 362-376, 2012
The present status and future growth of maintenance in US manufacturing: results from a pilot survey
X Jin, D Siegel, BA Weiss, E Gamel, W Wang, J Lee, J Ni
Manufacturing review 3, 2016
A comparative study on vibration‐based condition monitoring algorithms for wind turbine drive trains
D Siegel, W Zhao, E Lapira, M AbuAli, J Lee
Wind energy 17 (5), 695-714, 2014
A cyber physical interface for automation systems—methodology and examples
HA Kao, W Jin, D Siegel, J Lee
Machines 3 (2), 93-106, 2015
Present status and future growth of advanced maintenance technology and strategy in US manufacturing
X Jin, BA Weiss, D Siegel, J Lee
International journal of prognostics and health management 7 (Spec Iss on …, 2016
A systematic methodology for gearbox health assessment and fault classification
H Al-Atat, D Siegel, J Lee
Int J Prognostics Health Manage Soc 2 (1), 16, 2011
An auto-associative residual processing and K-means clustering approach for anemometer health assessment
D Siegel, J Lee
International Journal of Prognostics and Health Management Volume 2 (color), 117, 2011
A deviation based assessment methodology for multiple machine health patterns classification and fault detection
X Jia, C Jin, M Buzza, Y Di, D Siegel, J Lee
Mechanical Systems and Signal Processing 99, 244-261, 2018
An integrated framework of drivetrain degradation assessment and fault localization for offshore wind turbines
W Zhao, D Siegel, J Lee, L Su
Int. J. Progn. Health Manag 4, 46, 2013
PHM for railway system—A case study on the health assessment of the point machines
HD Ardakani, C Lucas, D Siegel, S Chang, P Dersin, B Bonnet, J Lee
2012 IEEE Conference on Prognostics and Health Management, 1-5, 2012
Development and evaluation of health monitoring techniques for railway point machines
W Jin, Z Shi, D Siegel, P Dersin, C Douziech, M Pugnaloni, P La Cascia, ...
2015 IEEE Conference on Prognostics and Health Management (PHM), 1-11, 2015
Development of a predictive and preventive maintenance demonstration system for a semiconductor etching tool
J Lee, D Siegel, ER Lapira
Ecs Transactions 52 (1), 913, 2013
Prognostics and health assessment of a multi-regime system using a residual clustering health monitoring approach
D Siegel
University of Cincinnati, 2013
Evaluation of health assessment techniques for rotating machinery
D Siegel
University of Cincinnati, 2009
Methodology and framework for predicting rolling element helicopter bearing failure
D Siegel, J Lee, C Ly
2011 IEEE Conference on Prognostics and Health Management, 1-9, 2011
Intelligent factory agents with predictive analytics for asset management
J Lee, HA Kao, HD Ardakani, D Siegel
Industrial Agents, 341-360, 2015
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