Joint Data-Driven Fault Diagnosis Integrating Causality Graph With Statistical Process Monitoring for Complex Industrial Processes
In this paper, an integrated fault diagnosis method is proposed to deal with fault location and propagation path identification. A causality graph is first constructed for the system according to the a priori knowledge. Afterward, a correlation index (CI) based on the partial correlation coefficient...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2017-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8082503/ |
| _version_ | 1818853814340419584 |
|---|---|
| author | Jie Dong Mengyuan Wang Xiong Zhang Liang Ma Kaixiang Peng |
| author_facet | Jie Dong Mengyuan Wang Xiong Zhang Liang Ma Kaixiang Peng |
| author_sort | Jie Dong |
| collection | DOAJ |
| description | In this paper, an integrated fault diagnosis method is proposed to deal with fault location and propagation path identification. A causality graph is first constructed for the system according to the a priori knowledge. Afterward, a correlation index (CI) based on the partial correlation coefficient is proposed to analyze the correlation of variables in causality graph quantitatively. To achieve accurate fault detection results, the proposed CI is monitored by probability principal component analysis. Moreover, the concept of weighted average value is introduced to identify fault propagation path based on reconstruction-based contribution and causality graph after detecting a fault. Finally, the new proposed scheme would be practiced with real industrial HSMP data, where the individual steps as well as the complete framework were extensively tested. |
| first_indexed | 2024-12-19T07:42:48Z |
| format | Article |
| id | doaj.art-84320d8493ca4a73bd6e57055ee42ede |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:42:48Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-84320d8493ca4a73bd6e57055ee42ede2022-12-21T20:30:25ZengIEEEIEEE Access2169-35362017-01-015252172522510.1109/ACCESS.2017.27662358082503Joint Data-Driven Fault Diagnosis Integrating Causality Graph With Statistical Process Monitoring for Complex Industrial ProcessesJie Dong0Mengyuan Wang1Xiong Zhang2Liang Ma3Kaixiang Peng4https://orcid.org/0000-0001-8314-3047Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaKey Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaKey Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaKey Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaKey Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaIn this paper, an integrated fault diagnosis method is proposed to deal with fault location and propagation path identification. A causality graph is first constructed for the system according to the a priori knowledge. Afterward, a correlation index (CI) based on the partial correlation coefficient is proposed to analyze the correlation of variables in causality graph quantitatively. To achieve accurate fault detection results, the proposed CI is monitored by probability principal component analysis. Moreover, the concept of weighted average value is introduced to identify fault propagation path based on reconstruction-based contribution and causality graph after detecting a fault. Finally, the new proposed scheme would be practiced with real industrial HSMP data, where the individual steps as well as the complete framework were extensively tested.https://ieeexplore.ieee.org/document/8082503/Joint data-drivenfault locationpropagation path identificationcausality graphPPCA |
| spellingShingle | Jie Dong Mengyuan Wang Xiong Zhang Liang Ma Kaixiang Peng Joint Data-Driven Fault Diagnosis Integrating Causality Graph With Statistical Process Monitoring for Complex Industrial Processes IEEE Access Joint data-driven fault location propagation path identification causality graph PPCA |
| title | Joint Data-Driven Fault Diagnosis Integrating Causality Graph With Statistical Process Monitoring for Complex Industrial Processes |
| title_full | Joint Data-Driven Fault Diagnosis Integrating Causality Graph With Statistical Process Monitoring for Complex Industrial Processes |
| title_fullStr | Joint Data-Driven Fault Diagnosis Integrating Causality Graph With Statistical Process Monitoring for Complex Industrial Processes |
| title_full_unstemmed | Joint Data-Driven Fault Diagnosis Integrating Causality Graph With Statistical Process Monitoring for Complex Industrial Processes |
| title_short | Joint Data-Driven Fault Diagnosis Integrating Causality Graph With Statistical Process Monitoring for Complex Industrial Processes |
| title_sort | joint data driven fault diagnosis integrating causality graph with statistical process monitoring for complex industrial processes |
| topic | Joint data-driven fault location propagation path identification causality graph PPCA |
| url | https://ieeexplore.ieee.org/document/8082503/ |
| work_keys_str_mv | AT jiedong jointdatadrivenfaultdiagnosisintegratingcausalitygraphwithstatisticalprocessmonitoringforcomplexindustrialprocesses AT mengyuanwang jointdatadrivenfaultdiagnosisintegratingcausalitygraphwithstatisticalprocessmonitoringforcomplexindustrialprocesses AT xiongzhang jointdatadrivenfaultdiagnosisintegratingcausalitygraphwithstatisticalprocessmonitoringforcomplexindustrialprocesses AT liangma jointdatadrivenfaultdiagnosisintegratingcausalitygraphwithstatisticalprocessmonitoringforcomplexindustrialprocesses AT kaixiangpeng jointdatadrivenfaultdiagnosisintegratingcausalitygraphwithstatisticalprocessmonitoringforcomplexindustrialprocesses |