| Image-based process monitoring using low-rank tensor decomposition H Yan, K Paynabar, J Shi IEEE Transactions on Automation Science and Engineering 12 (1), 216-227, 2014 | 109 | 2014 |
| Feature selection for manufacturing process monitoring using cross-validation C Shao, K Paynabar, TH Kim, JJ Jin, SJ Hu, JP Spicer, H Wang, JA Abell Journal of Manufacturing Systems 32 (4), 550-555, 2013 | 103 | 2013 |
| An overview and perspective on social network monitoring WH Woodall, MJ Zhao, K Paynabar, R Sparks, JD Wilson IISE Transactions 49 (3), 354-365, 2017 | 97 | 2017 |
| Analysis of multichannel nonlinear profiles using uncorrelated multilinear principal component analysis with applications in fault detection and diagnosis K Paynabar, J Jin, M Pacella IIE Transactions 45 (11), 1235-1247, 2013 | 88* | 2013 |
| A change-point approach for phase-I analysis in multivariate profile monitoring and diagnosis K Paynabar, C Zou, P Qiu Technometrics 58 (2), 191-204, 2016 | 87 | 2016 |
| Characterization of non-linear profiles variations using mixed-effect models and wavelets K Paynabar, J Jin Iie transactions 43 (4), 275-290, 2011 | 70 | 2011 |
| Anomaly detection in images with smooth background via smooth-sparse decomposition H Yan, K Paynabar, J Shi Technometrics 59 (1), 102-114, 2017 | 68 | 2017 |
| Phase I risk-adjusted control charts for monitoring surgical performance by considering categorical covariates K Paynabar, J Jin, AB Yeh Journal of Quality Technology 44 (1), 39-53, 2012 | 63 | 2012 |
| Monitoring temporal homogeneity in attributed network streams B Azarnoush, K Paynabar, J Bekki, G Runger Journal of Quality Technology 48 (1), 28-43, 2016 | 55 | 2016 |
| Real-time monitoring of high-dimensional functional data streams via spatio-temporal smooth sparse decomposition H Yan, K Paynabar, J Shi Technometrics 60 (2), 181-197, 2018 | 54 | 2018 |
| Multistream sensor fusion-based prognostics model for systems with single failure modes X Fang, K Paynabar, N Gebraeel Reliability Engineering & System Safety 159, 322-331, 2017 | 53 | 2017 |
| Identifying the period of a step change in high‐yield processes R Noorossana, A Saghaei, K Paynabar, S Abdi Quality and Reliability Engineering International 25 (7), 875-883, 2009 | 47 | 2009 |
| Repeatability of cerebral perfusion using dynamic susceptibility contrast MRI in glioblastoma patients K Jafari-Khouzani, KE Emblem, J Kalpathy-Cramer, A Bjørnerud, ... Translational oncology 8 (3), 137-146, 2015 | 42 | 2015 |
| Scalable prognostic models for large-scale condition monitoring applications X Fang, NZ Gebraeel, K Paynabar IISE Transactions 49 (7), 698-710, 2017 | 33 | 2017 |
| On the conditional decision procedure for high yield processes R Noorossana, A Saghaei, K Paynabar, Y Samimi Computers & Industrial Engineering 53 (3), 469-477, 2007 | 33 | 2007 |
| Multiscale monitoring of autocorrelated processes using wavelets analysis H Guo, K Paynabar, J Jin IIE Transactions 44 (4), 312-326, 2012 | 27 | 2012 |
| Multiple tensor-on-tensor regression: An approach for modeling processes with heterogeneous sources of data MR Gahrooei, H Yan, K Paynabar, J Shi Technometrics 63 (2), 147-159, 2021 | 25 | 2021 |
| Fast wavenumber measurement for accurate and automatic location and quantification of defect in composite O Mesnil, H Yan, M Ruzzene, K Paynabar, J Shi Structural Health Monitoring 15 (2), 223-234, 2016 | 25 | 2016 |
| Dataset: rare event classification in multivariate time series C Ranjan, M Reddy, M Mustonen, K Paynabar, K Pourak arXiv preprint arXiv:1809.10717, 2018 | 24 | 2018 |
| Change detection in a dynamic stream of attributed networks MR Gahrooei, K Paynabar Journal of Quality Technology 50 (4), 418-430, 2018 | 23 | 2018 |
