Fault diagnosis for a milk pasteurisation plant with missing data. Kadri, O., Mouss, L. H., & Abdelhadi, A. International Journal of Quality Engineering and Technology, 6(3):123–136, 2017. ![Fault diagnosis for a milk pasteurisation plant with missing data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This paper addresses the problem of fault diagnosis from observed data containing missing values amongst the inputs. In order to provide good classification accuracy for the decision function, a novel approach based on support vector machine and extreme learning machine is developed. SVM mixture model is used to model the data distribution, which is adapted to handle missing values, while extreme learning machine enables to devise a multiple imputation strategy for final estimation. In order to prove the efficiency of our proposed method, we have developed the classifier using the condition monitoring observations from milk pasteurisation data. The experiments show that the proposed algorithm gives improved results compared to recent methods, essentially if the number of missing data is significant. The results show that our approach can perfectly detect dysfunction, identify the fault, and is strong in unsupervised process monitoring.
@article{kadri_fault_2017,
title = {Fault diagnosis for a milk pasteurisation plant with missing data},
volume = {6},
url = {https://www.inderscienceonline.com/doi/abs/10.1504/IJQET.2017.088858},
doi = {10.1504/IJQET.2017.088858},
abstract = {This paper addresses the problem of fault diagnosis from observed data containing missing values amongst the inputs. In order to provide good classification accuracy for the decision function, a novel approach based on support vector machine and extreme learning machine is developed. SVM mixture model is used to model the data distribution, which is adapted to handle missing values, while extreme learning machine enables to devise a multiple imputation strategy for final estimation. In order to prove the efficiency of our proposed method, we have developed the classifier using the condition monitoring observations from milk pasteurisation data. The experiments show that the proposed algorithm gives improved results compared to recent methods, essentially if the number of missing data is significant. The results show that our approach can perfectly detect dysfunction, identify the fault, and is strong in unsupervised process monitoring.},
number = {3},
journal = {International Journal of Quality Engineering and Technology},
author = {Kadri, Ouahab and Mouss, L. H. and Abdelhadi, Adel},
year = {2017},
pages = {123--136},
}
Downloads: 0
{"_id":"F3Y2D3vajethB6ET3","bibbaseid":"kadri-mouss-abdelhadi-faultdiagnosisforamilkpasteurisationplantwithmissingdata-2017","downloads":0,"creationDate":"2018-04-04T10:13:44.655Z","title":"Fault diagnosis for a milk pasteurisation plant with missing data","author_short":["Kadri, O.","Mouss, L. H.","Abdelhadi, A."],"year":2017,"bibtype":"article","biburl":"https://api.zotero.org/groups/2158912/items?key=v9g3JsDHm0AINYlkHTBYHdpA&format=bibtex&limit=100","bibdata":{"bibtype":"article","type":"article","title":"Fault diagnosis for a milk pasteurisation plant with missing data","volume":"6","url":"https://www.inderscienceonline.com/doi/abs/10.1504/IJQET.2017.088858","doi":"10.1504/IJQET.2017.088858","abstract":"This paper addresses the problem of fault diagnosis from observed data containing missing values amongst the inputs. In order to provide good classification accuracy for the decision function, a novel approach based on support vector machine and extreme learning machine is developed. SVM mixture model is used to model the data distribution, which is adapted to handle missing values, while extreme learning machine enables to devise a multiple imputation strategy for final estimation. In order to prove the efficiency of our proposed method, we have developed the classifier using the condition monitoring observations from milk pasteurisation data. The experiments show that the proposed algorithm gives improved results compared to recent methods, essentially if the number of missing data is significant. The results show that our approach can perfectly detect dysfunction, identify the fault, and is strong in unsupervised process monitoring.","number":"3","journal":"International Journal of Quality Engineering and Technology","author":[{"propositions":[],"lastnames":["Kadri"],"firstnames":["Ouahab"],"suffixes":[]},{"propositions":[],"lastnames":["Mouss"],"firstnames":["L.","H."],"suffixes":[]},{"propositions":[],"lastnames":["Abdelhadi"],"firstnames":["Adel"],"suffixes":[]}],"year":"2017","pages":"123–136","bibtex":"@article{kadri_fault_2017,\n\ttitle = {Fault diagnosis for a milk pasteurisation plant with missing data},\n\tvolume = {6},\n\turl = {https://www.inderscienceonline.com/doi/abs/10.1504/IJQET.2017.088858},\n\tdoi = {10.1504/IJQET.2017.088858},\n\tabstract = {This paper addresses the problem of fault diagnosis from observed data containing missing values amongst the inputs. In order to provide good classification accuracy for the decision function, a novel approach based on support vector machine and extreme learning machine is developed. SVM mixture model is used to model the data distribution, which is adapted to handle missing values, while extreme learning machine enables to devise a multiple imputation strategy for final estimation. In order to prove the efficiency of our proposed method, we have developed the classifier using the condition monitoring observations from milk pasteurisation data. The experiments show that the proposed algorithm gives improved results compared to recent methods, essentially if the number of missing data is significant. The results show that our approach can perfectly detect dysfunction, identify the fault, and is strong in unsupervised process monitoring.},\n\tnumber = {3},\n\tjournal = {International Journal of Quality Engineering and Technology},\n\tauthor = {Kadri, Ouahab and Mouss, L. H. and Abdelhadi, Adel},\n\tyear = {2017},\n\tpages = {123--136},\n}\n\n","author_short":["Kadri, O.","Mouss, L. H.","Abdelhadi, A."],"key":"kadri_fault_2017","id":"kadri_fault_2017","bibbaseid":"kadri-mouss-abdelhadi-faultdiagnosisforamilkpasteurisationplantwithmissingdata-2017","role":"author","urls":{"Paper":"https://www.inderscienceonline.com/doi/abs/10.1504/IJQET.2017.088858"},"metadata":{"authorlinks":{}},"downloads":0},"search_terms":["fault","diagnosis","milk","pasteurisation","plant","missing","data","kadri","mouss","abdelhadi"],"keywords":[],"authorIDs":[],"dataSources":["o6H2FdNemGj2A9Rsq"]}