David Siegel
David Siegel
Center for Intelligent Maintenance Systems
Verified email at mail.uc.edu
Title
Cited by
Cited by
Year
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
11432014
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
4582008
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
1422012
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
692012
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
692012
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
592016
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
562014
A cyber physical interface for automation systems—methodology and examples
HA Kao, W Jin, D Siegel, J Lee
Machines 3 (2), 93-106, 2015
542015
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
512016
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
432011
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
422011
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
402018
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
322013
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
292012
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
272015
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
172013
Prognostics and health assessment of a multi-regime system using a residual clustering health monitoring approach
D Siegel
University of Cincinnati, 2013
172013
Evaluation of health assessment techniques for rotating machinery
D Siegel
University of Cincinnati, 2009
152009
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
142011
Intelligent factory agents with predictive analytics for asset management
J Lee, HA Kao, HD Ardakani, D Siegel
Industrial Agents, 341-360, 2015
122015
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