The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Kernel partial least squares regression in reproducing kernel hilbert space
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Improved kernel fisher discriminant analysis for fault diagnosis
Expert Systems with Applications: An International Journal
A Maximum Class Distance Support Vector Machine-Based Algorithm for Recursive Dimension Reduction
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
A probabilistic SVM based decision system for pain diagnosis
Expert Systems with Applications: An International Journal
Automated recognition of robotic manipulation failures in high-throughput biodosimetry tool
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
Fault identification is one of essential operational tasks required for process safety and consistent production of high quality final products. The objective of fault identification is to identify process variables responsible for causing a specific fault in the process. Such an identification of contributing process variables helps process operators or engineers to diagnose a root cause of the fault more effectively. A new nonlinear fault identification method is developed using a nonlinear kernel-based Fisher discriminant analysis (KFDA). The proposed method performs a pair-wise KFDA on normal and fault data. Thus it characterizes the change of each process variable's contribution relative to normal operating conditions when a specific fault occurs. A case study on the Tennessee Eastman process has shown that the proposed method produces reliable identification results. Moreover, the proposed method outperforms the contribution chart approach based on linear PCA. The use of a nonlinear technique of KFDA in a fault identification task was shown to be a promising tool for determining key process variables of various faults.