Name
Abstract | ||
|---|---|---|
Dimensionality reduction techniques including partial least-squares (PLS) and principal component analysis (PCA) have been widely applied for data-driven process monitoring. However, the objectives of PCA- and PLS-based techniques are not specific for fault detection where a superior detection performance results from a large divergence (i.e., difference) between normal operating data and faulty data. In this article, a maximized divergence analysis (MDA) method is proposed to detect faults in industrial systems. The objective of MDA is to directly maximizes the Kullback-Leibler (KL) divergence corresponding to the distributions of normal operating data and faulty data during the procedure of dimensionality reduction. An algorithm using eigenvalue-decomposition technique is put forward to efficiently solve the optimization problem of maximizing KL-divergence. Two-dimensional synthetic data and Tennessee Eastman process are used to demonstrate the effectiveness of the proposed MDA-based detection approach. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ACCESS.2022.3181360 | IEEE ACCESS |
Keywords | DocType | Volume |
Fault detection, Dimensionality reduction, Principal component analysis, Loading, Process monitoring, Probability density function, Optimization, Dimensionality reduction technique, fault detection, fault diagnosis, process monitoring, Kullback-Leibler divergence, Tennessee Eastman process | Journal | 10 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benben Jiang | 1 | 0 | 0.68 |
| Qiugang Lu | 2 | 0 | 0.68 |