TY - JOUR
T1 - An efficient ICA-DW-SVDD fault detection and diagnosis method for non-Gaussian processes
AU - Chen, Mu-Chen
AU - Hsu, Chun Chin
AU - Malhotra, Bharat
AU - Tiwari, Manoj Kumar
PY - 2016/9/1
Y1 - 2016/9/1
N2 - Independent Component Analysis (ICA) has been extensively used for detecting faults in industrial processes. While applying ICA to process monitoring, the inability of identifying the important components affect the fault diagnosis ability. For further improving the competence of ICA, this paper proposes an approach integrating ICA, Durbin Watson (DW) criterion and Support Vector Data Description (SVDD) to monitor non-Gaussian process for detecting faults. In the proposed approach, namely ICA–DW–SVDD, ICA is a non-Gaussian information extractor from original variables, DW identifies dominating ICs, and SVDD plays the role of fault detector. This paper also discusses the retracing method to detect original variables causing disturbance in the process. One simulation case and the Tennessee Eastman Process are used to demonstrate the effectiveness of our proposed approach.
AB - Independent Component Analysis (ICA) has been extensively used for detecting faults in industrial processes. While applying ICA to process monitoring, the inability of identifying the important components affect the fault diagnosis ability. For further improving the competence of ICA, this paper proposes an approach integrating ICA, Durbin Watson (DW) criterion and Support Vector Data Description (SVDD) to monitor non-Gaussian process for detecting faults. In the proposed approach, namely ICA–DW–SVDD, ICA is a non-Gaussian information extractor from original variables, DW identifies dominating ICs, and SVDD plays the role of fault detector. This paper also discusses the retracing method to detect original variables causing disturbance in the process. One simulation case and the Tennessee Eastman Process are used to demonstrate the effectiveness of our proposed approach.
KW - independent component analysis
KW - process control
KW - statistical process control (SPC)
KW - support vector data description
UR - http://www.scopus.com/inward/record.url?scp=84961113592&partnerID=8YFLogxK
U2 - 10.1080/00207543.2016.1161250
DO - 10.1080/00207543.2016.1161250
M3 - Article
AN - SCOPUS:84961113592
VL - 54
SP - 5208
EP - 5218
JO - International Journal of Production Research
JF - International Journal of Production Research
SN - 0020-7543
IS - 17
ER -