Estimation of Disturbance Propagation Path Using Principal Component Analysis (PCA) and Multivariate Granger Causality (MVGC) Techniques
| Autor: | Nadeem Shaukat, Umer Zahid, Chonghun Han, Seolin Shin, Usama Ahmed, Daegeun Ha |
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| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Computer science Covariance matrix General Chemical Engineering Hardware_PERFORMANCEANDRELIABILITY 02 engineering and technology General Chemistry Process variable Residual Fault (power engineering) Industrial and Manufacturing Engineering Fault detection and isolation Computer Science::Hardware Architecture 020901 industrial engineering & automation 020401 chemical engineering Singular value decomposition Principal component analysis 0204 chemical engineering Computer Science::Operating Systems Algorithm Computer Science::Distributed Parallel and Cluster Computing Subspace topology |
| Zdroj: | Industrial & Engineering Chemistry Research. 56:7260-7272 |
| ISSN: | 1520-5045 0888-5885 |
| DOI: | 10.1021/acs.iecr.6b02763 |
| Popis: | Process monitoring and fault diagnosis using the multivariate statistical methodologies has been extensively used in the process and product development industries for the last several decades. The fault in one process variable readily affects all the other associated variables, which makes the fault detection process not only more difficult but also time-consuming. In this study, principal component analysis (PCA)-based fault amplification algorithm is developed to detect both the root cause of fault and the fault propagation path in the system. The developed algorithm projects the samples on the residual subspace (RS) to determine the disturbance propagation path. Usually, the RS of the fault data is superimposed with the normal process variations, which should be minimized to amplify the fault magnitude. The RS-containing amplified fault is then converted to the covariance matrix, followed by singular value decomposition (SVD) analysis, which, in turn, generates the fault direction matrix corresponding... |
| Databáze: | OpenAIRE |
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