Fault diagnosis using contribution plots without smearing effect on non-faulty variables. Liu, J. Journal of Process Control, 22(9):1609–1623, October, 2012. ![Fault diagnosis using contribution plots without smearing effect on non-faulty variables [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Isolating faulty variables to provide additional information about a process fault is a crucial step in the diagnosis of a process fault. There are two types of data-driven approaches for isolating faulty variables. One is the supervised method, which requires the datasets of known faults to define a fault subspace or an abnormal operating region for each faulty mode. This type of approach is not practical for an industrial process, since the known event lists might not exist for some industrial processes. The counterpart is to isolate faulty variables without a priori knowledge, using, for example, a contribution plot, which is a popular tool in the unsupervised category. However, it is well known that this approach suffers from the smearing effect, which may mislead the faulty variables of the detected faults. In the presented work, a contribution plot without the smearing effect on non-faulty variables was derived based on missing data analysis. Two benchmark examples, the continuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) process, were provided to compare the fault isolation performances of the alternatives using the missing data approach.
@article{liu_fault_2012,
title = {Fault diagnosis using contribution plots without smearing effect on non-faulty variables},
volume = {22},
issn = {0959-1524},
url = {https://www.sciencedirect.com/science/article/pii/S0959152412001576},
doi = {10.1016/j.jprocont.2012.06.016},
abstract = {Isolating faulty variables to provide additional information about a process fault is a crucial step in the diagnosis of a process fault. There are two types of data-driven approaches for isolating faulty variables. One is the supervised method, which requires the datasets of known faults to define a fault subspace or an abnormal operating region for each faulty mode. This type of approach is not practical for an industrial process, since the known event lists might not exist for some industrial processes. The counterpart is to isolate faulty variables without a priori knowledge, using, for example, a contribution plot, which is a popular tool in the unsupervised category. However, it is well known that this approach suffers from the smearing effect, which may mislead the faulty variables of the detected faults. In the presented work, a contribution plot without the smearing effect on non-faulty variables was derived based on missing data analysis. Two benchmark examples, the continuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) process, were provided to compare the fault isolation performances of the alternatives using the missing data approach.},
language = {en},
number = {9},
urldate = {2022-05-02},
journal = {Journal of Process Control},
author = {Liu, Jialin},
month = oct,
year = {2012},
keywords = {Contribution plots, Fault isolation, Missing data analysis, Principal component analysis},
pages = {1609--1623},
}
Downloads: 0
{"_id":"JYHwQSLo7dnsFm5by","bibbaseid":"liu-faultdiagnosisusingcontributionplotswithoutsmearingeffectonnonfaultyvariables-2012","author_short":["Liu, J."],"bibdata":{"bibtype":"article","type":"article","title":"Fault diagnosis using contribution plots without smearing effect on non-faulty variables","volume":"22","issn":"0959-1524","url":"https://www.sciencedirect.com/science/article/pii/S0959152412001576","doi":"10.1016/j.jprocont.2012.06.016","abstract":"Isolating faulty variables to provide additional information about a process fault is a crucial step in the diagnosis of a process fault. There are two types of data-driven approaches for isolating faulty variables. One is the supervised method, which requires the datasets of known faults to define a fault subspace or an abnormal operating region for each faulty mode. This type of approach is not practical for an industrial process, since the known event lists might not exist for some industrial processes. The counterpart is to isolate faulty variables without a priori knowledge, using, for example, a contribution plot, which is a popular tool in the unsupervised category. However, it is well known that this approach suffers from the smearing effect, which may mislead the faulty variables of the detected faults. In the presented work, a contribution plot without the smearing effect on non-faulty variables was derived based on missing data analysis. Two benchmark examples, the continuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) process, were provided to compare the fault isolation performances of the alternatives using the missing data approach.","language":"en","number":"9","urldate":"2022-05-02","journal":"Journal of Process Control","author":[{"propositions":[],"lastnames":["Liu"],"firstnames":["Jialin"],"suffixes":[]}],"month":"October","year":"2012","keywords":"Contribution plots, Fault isolation, Missing data analysis, Principal component analysis","pages":"1609–1623","bibtex":"@article{liu_fault_2012,\n\ttitle = {Fault diagnosis using contribution plots without smearing effect on non-faulty variables},\n\tvolume = {22},\n\tissn = {0959-1524},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0959152412001576},\n\tdoi = {10.1016/j.jprocont.2012.06.016},\n\tabstract = {Isolating faulty variables to provide additional information about a process fault is a crucial step in the diagnosis of a process fault. There are two types of data-driven approaches for isolating faulty variables. One is the supervised method, which requires the datasets of known faults to define a fault subspace or an abnormal operating region for each faulty mode. This type of approach is not practical for an industrial process, since the known event lists might not exist for some industrial processes. The counterpart is to isolate faulty variables without a priori knowledge, using, for example, a contribution plot, which is a popular tool in the unsupervised category. However, it is well known that this approach suffers from the smearing effect, which may mislead the faulty variables of the detected faults. In the presented work, a contribution plot without the smearing effect on non-faulty variables was derived based on missing data analysis. Two benchmark examples, the continuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) process, were provided to compare the fault isolation performances of the alternatives using the missing data approach.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-05-02},\n\tjournal = {Journal of Process Control},\n\tauthor = {Liu, Jialin},\n\tmonth = oct,\n\tyear = {2012},\n\tkeywords = {Contribution plots, Fault isolation, Missing data analysis, Principal component analysis},\n\tpages = {1609--1623},\n}\n\n\n\n","author_short":["Liu, J."],"key":"liu_fault_2012","id":"liu_fault_2012","bibbaseid":"liu-faultdiagnosisusingcontributionplotswithoutsmearingeffectonnonfaultyvariables-2012","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/S0959152412001576"},"keyword":["Contribution plots","Fault isolation","Missing data analysis","Principal component analysis"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["contribution plots","fault isolation","missing data analysis","principal component analysis"],"search_terms":["fault","diagnosis","using","contribution","plots","without","smearing","effect","non","faulty","variables","liu"],"title":"Fault diagnosis using contribution plots without smearing effect on non-faulty variables","year":2012}