Name
Abstract | ||
|---|---|---|
Principal component analysis (PCA) has been widely applied in process monitoring and modeling. The time-varying property of industrial processes requires the adaptive ability of the PCA. This paper introduces a novel PCA algorithm, named on-line PCA (OLPCA). It updates the PCA model according to the process status. The approximate linear dependence (ALD) condition is used to check each new sample. A recursive algorithm is proposed to reconstruct the PCA model with selected samples. Three types of experiments, a synthetic data, a benchmark problem, and a ball mill load experimental data, are used to illustrate our modeling method. The results show that the proposed OLPCA is computationally faster, and the modeling accuracy is higher than conventional moving window PCA (MWPCA) and recursive PCA (RPCA) for time-varying process modeling. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.neucom.2011.10.026 | Neurocomputing |
Keywords | Field | DocType |
pca model,on-line pca,on-line principal component analysis,modeling method,modeling accuracy,recursive pca,process monitoring,time-varying process modeling,industrial process,window pca,novel pca algorithm,principal component analysis,recursive algorithm,process modeling | Sparse PCA,Recursion (computer science),Pattern recognition,Experimental data,Work in process,Computer science,Process modeling,Synthetic data,Artificial intelligence,Machine learning,Principal component analysis,Recursion | Journal |
Volume | ISSN | Citations |
82, | 0925-2312 | 2 |
PageRank | References | Authors |
0.48 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Tang | 1 | 526 | 148.30 |
| Wen Yu | 2 | 246 | 52.12 |
| Tianyou Chai | 3 | 2014 | 175.55 |
| Lijie Zhao | 4 | 41 | 9.72 |