| 1 |
An Improved Non-negative Matrix Factorization Method for Dynamic Industrial Fault Diagnosis
Gong XF, Sun DD, Tang ZD, Zhou K, Luo RS
Journal of Chemical Engineering of Japan, 53(7), 321, 2020 |
| 2 |
Mixed kernel canonical variate dissimilarity analysis for incipient fault monitoring in nonlinear dynamic processes
Pilario KES, Cao Y, Shafiee M
Computers & Chemical Engineering, 123, 143, 2019 |
| 3 |
Efficient recursive kernel canonical variate analysis for monitoring nonlinear time-varying processes
Shang LL, Liu JC, Zhang YW
Canadian Journal of Chemical Engineering, 96(1), 205, 2018 |
| 4 |
Locality preserving discriminative canonical variate analysis for fault diagnosis
Lu QG, Jiang BB, Gopaluni RB, Loewen PD, Braatz RD
Computers & Chemical Engineering, 117, 309, 2018 |
| 5 |
Sparse canonical variate analysis approach for process monitoring
Lu QG, Jiang BB, Gopaluni RB, Loewen PD, Braatz RD
Journal of Process Control, 71, 90, 2018 |
| 6 |
Fault detection of process correlation structure using canonical variate analysis-based correlation features
Jiang BB, Braatz RD
Journal of Process Control, 58, 131, 2017 |
| 7 |
A combined canonical variate analysis and Fisher discriminant analysis (CVA-FDA) approach for fault diagnosis
Jiang BB, Zhu XX, Huang DX, Paulson JA, Braatz RD
Computers & Chemical Engineering, 77, 1, 2015 |
| 8 |
Canonical variate analysis-based contributions for fault identification
Jiang BB, Huang DX, Zhu XX, Yang F, Braatz RD
Journal of Process Control, 26, 17, 2015 |
| 9 |
Canonical variate analysis-based monitoring of process correlation structure using causal feature representation
Jiang BB, Zhu XX, Huang DX, Braatz RD
Journal of Process Control, 32, 109, 2015 |
| 10 |
A Multi-SOM with Canonical Variate Analysis for Chemical Process Monitoring and Fault Diagnosis
Song Y, Jiang QC, Yan XF, Guo MJ
Journal of Chemical Engineering of Japan, 47(1), 40, 2014 |
