| 1 |
Simplified Granger causality map for data-driven root cause diagnosis of process disturbances
Liu Y, Chen HS, Wu HB, Dai Y, Yao Y, Yan ZB
Journal of Process Control, 95, 45, 2020 |
| 2 |
Fault detection and pathway analysis using a dynamic Bayesian network
Amin MT, Khan F, Imtiaz S
Chemical Engineering Science, 195, 777, 2019 |
| 3 |
Comparative analysis of Granger causality and transfer entropy to present a decision flow for the application of oscillation diagnosis
Lindner B, Auret L, Bauer M, Groenewald JWD
Journal of Process Control, 79, 72, 2019 |
| 4 |
A review of control loop monitoring and diagnosis: Prospects of controller maintenance in big data era
Gao XQ, Yang F, Shang C, Huang DX
Chinese Journal of Chemical Engineering, 24(8), 952, 2016 |
| 5 |
Methods for root cause diagnosis of plant-wide oscillations
Duan P, Chen TW, Shah SL, Yang F
AIChE Journal, 60(6), 2019, 2014 |
| 6 |
Identification of probabilistic graphical network model for root-cause diagnosis in industrial processes
Mori J, Mahalec V, Yu J
Computers & Chemical Engineering, 71, 171, 2014 |
| 7 |
Root cause diagnosis of plant-wide oscillations using Granger causality
Yuan T, Qin SJ
Journal of Process Control, 24(2), 450, 2014 |
| 8 |
A novel dynamic bayesian network-based networked process monitoring approach for fault detection, propagation identification, and root cause diagnosis
Yu J, Rashid MM
AIChE Journal, 59(7), 2348, 2013 |
