| Introduction to time series analysis and forecasting DC Montgomery, CL Jennings, M Kulahci John Wiley & Sons, 2015 | 2935 | 2015 |
| Time series analysis and forecasting by example S Bisgaard, M Kulahci John Wiley & Sons, 2011 | 386 | 2011 |
| Real-time fault detection and diagnosis using sparse principal component analysis S Gajjar, M Kulahci, A Palazoglu Journal of Process Control 67, 112-128, 2018 | 159 | 2018 |
| On the structure of dynamic principal component analysis used in statistical process monitoring E Vanhatalo, M Kulahci, B Bergquist Chemometrics and intelligent laboratory systems 167, 1-11, 2017 | 105 | 2017 |
| A taxonomy of railway track maintenance planning and scheduling: A review and research trends M Sedghi, O Kauppila, B Bergquist, E Vanhatalo, M Kulahci Reliability Engineering & System Safety 215, 107827, 2021 | 93 | 2021 |
| An extended Tennessee Eastman simulation dataset for fault-detection and decision support systems C Reinartz, M Kulahci, O Ravn Computers & chemical engineering 149, 107281, 2021 | 82 | 2021 |
| The Effect of Autocorrelation on the Hotelling T2 Control Chart E Vanhatalo, M Kulahci Quality and Reliability Engineering International 31 (8), 1779-1796, 2015 | 71 | 2015 |
| The role of big data in industrial (bio) chemical process operations IA Udugama, CL Gargalo, Y Yamashita, MA Taube, A Palazoglu, ... Industrial & Engineering Chemistry Research 59 (34), 15283-15297, 2020 | 59 | 2020 |
| Impact of autocorrelation on principal components and their use in statistical process control E Vanhatalo, M Kulahci Quality and Reliability Engineering International 32 (4), 1483-1500, 2016 | 58 | 2016 |
| Quality quandaries: the effect of autocorrelation on statistical process control procedures S Bisgaard, M Kulahci Quality Engineering 17 (3), 481-489, 2005 | 54 | 2005 |
| The revised Tennessee Eastman process simulator as testbed for SPC and DoE methods F Capaci, E Vanhatalo, M Kulahci, B Bergquist Quality Engineering 31 (2), 212-229, 2019 | 51 | 2019 |
| A novel fault detection and diagnosis approach based on orthogonal autoencoders D Cacciarelli, M Kulahci Computers & Chemical Engineering 163, 107853, 2022 | 49 | 2022 |
| Active learning for data streams: a survey D Cacciarelli, M Kulahci Machine Learning 113 (1), 185-239, 2024 | 45 | 2024 |
| Recent advances and future directions for quality engineering G Vining, M Kulahci, S Pedersen Quality and Reliability Engineering International 32 (3), 863-875, 2016 | 43 | 2016 |
| Monitoring pig movement at the slaughterhouse using optical flow and modified angular histograms R Gronskyte, LH Clemmensen, MS Hviid, M Kulahci Biosystems Engineering 141, 19-30, 2016 | 43 | 2016 |
| Pig herd monitoring and undesirable tripping and stepping prevention R Gronskyte, LH Clemmensen, MS Hviid, M Kulahci Computers and Electronics in Agriculture 119, 51-60, 2015 | 43 | 2015 |
| The use of Plackett–Burman designs to construct split-plot designs M Kulahci, S Bisgaard Technometrics 47 (4), 495-501, 2005 | 38 | 2005 |
| Selection of non-zero loadings in sparse principal component analysis S Gajjar, M Kulahci, A Palazoglu Chemometrics and Intelligent Laboratory Systems 162, 160-171, 2017 | 36 | 2017 |
| A numerical study of Markov decision process algorithms for multi-component replacement problems JF Andersen, AR Andersen, M Kulahci, BF Nielsen European Journal of Operational Research 299 (3), 898-909, 2022 | 35 | 2022 |
| Cost-sensitive learning classification strategy for predicting product failures FD Frumosu, AR Khan, H Schiøler, M Kulahci, M Zaki, ... Expert Systems with Applications 161, 113653, 2020 | 35 | 2020 |
