| 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 |
Linear programming based time lag identification in event sequences
Huber MF, Zoller MA, Baum M
Automatica, 98, 14, 2018 |
| 5 |
A study of accident investigation methodologies applied to the Natech events during the 2011 Great East Japan. earthquake
Chakraborty A, Ibrahim A, Cruz AM
Journal of Loss Prevention in The Process Industries, 51, 208, 2018 |
| 6 |
Monitoring of a simulated milling circuit: Fault diagnosis and economic impact
Wakefield BJ, Lindner BS, McCoy JT, Auret L
Minerals Engineering, 120, 132, 2018 |
| 7 |
Analysis of spring operated pressure relief valve proof test data: Findings and implications
Bukowski JV, Goble WM, Gross RE, Harris SP
Process Safety Progress, 37(4), 467, 2018 |
| 8 |
SEMIPARAMETRIC PCA AND BETWEEN BAYESIAN NETWORK BASED PROCESS FAULT DIAGNOSIS TECHNIQUE
Wang YZ, Liu Y, Khan F, Imtiaz S
Canadian Journal of Chemical Engineering, 95(9), 1800, 2017 |
| 9 |
Refined convergent cross-mapping for disturbance propagation analysis of chemical processes
Luo L, Cheng FF, Qiu T, Zhao JS
Computers & Chemical Engineering, 106, 1, 2017 |
| 10 |
A study of caprolactam storage tank accident through root cause analysis with a computational approach
Liu WY, Chen CH, Chen WT, Shu CM
Journal of Loss Prevention in The Process Industries, 50, 80, 2017 |
