A method of fault diagnosis based on PCA and bayes classification

A method of fault diagnosis based on PCA and bayes classification. Shi, X., Liang, J., Ye, L., & Hu, B. In Proceedings of the World Congress on Intelligent Control and Automation (WCICA), pages 5628-5631, 2010.
doi abstract bibtex

By Principal Components Analysis (PCA) method, we can extract the main element from the fault sample set to obtain reduced feature space, which is suitable for fault diagnosis. Bayes method has shown its good classification performance in fault diagnosis, while the real-timing of this method can be guaranteed effectively. By taking advantages of the PCA and Naive Bayes classification, an integrated approach is proposed for the fault diagnosis of chemical process. Firstly, the dimension of industrial data was reduced by PCA method, and the resulting data were discretized to some grades for Bayes classification. The simulation results of TE process show that PCA-Bayes classification is feasible to detect and locate faults quickly with good real time property and high robustness. © 2010 IEEE.

@inproceedings{
 title = {A method of fault diagnosis based on PCA and bayes classification},
 type = {inproceedings},
 year = {2010},
 keywords = {Fault diagnosis,Naive bayes classification,Principal components analysis,TE process},
 pages = {5628-5631},
 id = {5761503c-c09f-37f8-8d74-98efdad8b65b},
 created = {2020-07-31T17:58:45.926Z},
 file_attached = {false},
 profile_id = {c23b2020-6c99-31d5-8474-4a35eb1af667},
 last_modified = {2021-04-09T16:14:42.092Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {By Principal Components Analysis (PCA) method, we can extract the main element from the fault sample set to obtain reduced feature space, which is suitable for fault diagnosis. Bayes method has shown its good classification performance in fault diagnosis, while the real-timing of this method can be guaranteed effectively. By taking advantages of the PCA and Naive Bayes classification, an integrated approach is proposed for the fault diagnosis of chemical process. Firstly, the dimension of industrial data was reduced by PCA method, and the resulting data were discretized to some grades for Bayes classification. The simulation results of TE process show that PCA-Bayes classification is feasible to detect and locate faults quickly with good real time property and high robustness. © 2010 IEEE.},
 bibtype = {inproceedings},
 author = {Shi, Xiangrong and Liang, Jun and Ye, Lubin and Hu, Bin},
 doi = {10.1109/WCICA.2010.5554741},
 booktitle = {Proceedings of the World Congress on Intelligent Control and Automation (WCICA)}
}

Downloads: 0

{"_id":"9GAucrzThow4wPd7z","bibbaseid":"shi-liang-ye-hu-amethodoffaultdiagnosisbasedonpcaandbayesclassification-2010","authorIDs":["PLEsBgAhit996P7kG","dt2MbKdppN7jFk7YN"],"author_short":["Shi, X.","Liang, J.","Ye, L.","Hu, B."],"bibdata":{"title":"A method of fault diagnosis based on PCA and bayes classification","type":"inproceedings","year":"2010","keywords":"Fault diagnosis,Naive bayes classification,Principal components analysis,TE process","pages":"5628-5631","id":"5761503c-c09f-37f8-8d74-98efdad8b65b","created":"2020-07-31T17:58:45.926Z","file_attached":false,"profile_id":"c23b2020-6c99-31d5-8474-4a35eb1af667","last_modified":"2021-04-09T16:14:42.092Z","read":false,"starred":false,"authored":"true","confirmed":"true","hidden":false,"private_publication":false,"abstract":"By Principal Components Analysis (PCA) method, we can extract the main element from the fault sample set to obtain reduced feature space, which is suitable for fault diagnosis. Bayes method has shown its good classification performance in fault diagnosis, while the real-timing of this method can be guaranteed effectively. By taking advantages of the PCA and Naive Bayes classification, an integrated approach is proposed for the fault diagnosis of chemical process. Firstly, the dimension of industrial data was reduced by PCA method, and the resulting data were discretized to some grades for Bayes classification. The simulation results of TE process show that PCA-Bayes classification is feasible to detect and locate faults quickly with good real time property and high robustness. © 2010 IEEE.","bibtype":"inproceedings","author":"Shi, Xiangrong and Liang, Jun and Ye, Lubin and Hu, Bin","doi":"10.1109/WCICA.2010.5554741","booktitle":"Proceedings of the World Congress on Intelligent Control and Automation (WCICA)","bibtex":"@inproceedings{\n title = {A method of fault diagnosis based on PCA and bayes classification},\n type = {inproceedings},\n year = {2010},\n keywords = {Fault diagnosis,Naive bayes classification,Principal components analysis,TE process},\n pages = {5628-5631},\n id = {5761503c-c09f-37f8-8d74-98efdad8b65b},\n created = {2020-07-31T17:58:45.926Z},\n file_attached = {false},\n profile_id = {c23b2020-6c99-31d5-8474-4a35eb1af667},\n last_modified = {2021-04-09T16:14:42.092Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {By Principal Components Analysis (PCA) method, we can extract the main element from the fault sample set to obtain reduced feature space, which is suitable for fault diagnosis. Bayes method has shown its good classification performance in fault diagnosis, while the real-timing of this method can be guaranteed effectively. By taking advantages of the PCA and Naive Bayes classification, an integrated approach is proposed for the fault diagnosis of chemical process. Firstly, the dimension of industrial data was reduced by PCA method, and the resulting data were discretized to some grades for Bayes classification. The simulation results of TE process show that PCA-Bayes classification is feasible to detect and locate faults quickly with good real time property and high robustness. © 2010 IEEE.},\n bibtype = {inproceedings},\n author = {Shi, Xiangrong and Liang, Jun and Ye, Lubin and Hu, Bin},\n doi = {10.1109/WCICA.2010.5554741},\n booktitle = {Proceedings of the World Congress on Intelligent Control and Automation (WCICA)}\n}","author_short":["Shi, X.","Liang, J.","Ye, L.","Hu, B."],"biburl":"https://bibbase.org/service/mendeley/c23b2020-6c99-31d5-8474-4a35eb1af667","bibbaseid":"shi-liang-ye-hu-amethodoffaultdiagnosisbasedonpcaandbayesclassification-2010","role":"author","urls":{},"keyword":["Fault diagnosis","Naive bayes classification","Principal components analysis","TE process"],"metadata":{"authorlinks":{"hu, b":"https://bibbase.org/show?bib=https%3A%2F%2Fwww.lions.odu.edu%2F%7Ebhu%2Fmypub.bib&msg=embed","hu, b":"https://bibbase.org/service/mendeley/c23b2020-6c99-31d5-8474-4a35eb1af667"}},"downloads":0},"bibtype":"inproceedings","creationDate":"2021-02-26T09:02:22.598Z","downloads":0,"keywords":["fault diagnosis","naive bayes classification","principal components analysis","te process"],"search_terms":["method","fault","diagnosis","based","pca","bayes","classification","shi","liang","ye","hu"],"title":"A method of fault diagnosis based on PCA and bayes classification","year":2010,"biburl":"https://bibbase.org/service/mendeley/c23b2020-6c99-31d5-8474-4a35eb1af667","dataSources":["f5RAvkrA9msarzLfE","ya2CyA73rpZseyrZ8","2252seNhipfTmjEBQ","okS2ihRW33d7QPvsN"]}