Explicit Representation of Mechanical Functions for Maintenance Decision Support
Artificial intelligence (AI) has been increasingly applied to condition-based maintenance (CBM), a knowledge-based method taking advantage of human expertise and other system knowledge that can serve as an alternative in cases in which machine learning is inapplicable due to a lack of training data....
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/20/4267 |
| _version_ | 1797574098921979904 |
|---|---|
| author | Mengchu Song Ilmar F. Santos Xinxin Zhang Jing Wu Morten Lind |
| author_facet | Mengchu Song Ilmar F. Santos Xinxin Zhang Jing Wu Morten Lind |
| author_sort | Mengchu Song |
| collection | DOAJ |
| description | Artificial intelligence (AI) has been increasingly applied to condition-based maintenance (CBM), a knowledge-based method taking advantage of human expertise and other system knowledge that can serve as an alternative in cases in which machine learning is inapplicable due to a lack of training data. Functional information is seen as the most fundamental and important knowledge in maintenance decision making. This paper first proposes a mechanical functional modeling approach based on a functional modeling and reasoning methodology called multilevel flow modeling (MFM). The approach actually bridges the modeling gap between the mechanical level and the process level, which potentially extends the existing capability of MFM in rule-based diagnostics and prognostics from operation support to maintenance support. Based on this extension, a framework of optimized CBM is proposed, which can be used to diagnose potential mechanical failures from condition monitoring data and predict their future impacts in a qualitative way. More importantly, the framework uses MFM-based reliability-centered maintenance (RCM) to determine the importance of a detected potential failure, which can ensure the cost-effectiveness of CBM by adapting the maintenance requirements to specific operational contexts. This ability cannot be offered by existing CBM methods. An application to a mechanical test apparatus and hypothetical coupling with a process plant are used to demonstrate the proposed framework. |
| first_indexed | 2024-03-10T21:18:07Z |
| format | Article |
| id | doaj.art-eff9b1d238da41739a5bbf8011e6ca8c |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T21:18:07Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-eff9b1d238da41739a5bbf8011e6ca8c2023-11-19T16:19:13ZengMDPI AGElectronics2079-92922023-10-011220426710.3390/electronics12204267Explicit Representation of Mechanical Functions for Maintenance Decision SupportMengchu Song0Ilmar F. Santos1Xinxin Zhang2Jing Wu3Morten Lind4Department of Electrical and Photonics Engineering, Technical University of Denmark, 2800 Kongens Lyngby, DenmarkDepartment of Civil and Mechanical Engineering, Technical University of Denmark, 2800 Kongens Lyngby, DenmarkDepartment of Electrical and Photonics Engineering, Technical University of Denmark, 2800 Kongens Lyngby, DenmarkDepartment of Electrical and Photonics Engineering, Technical University of Denmark, 2800 Kongens Lyngby, DenmarkDepartment of Electrical and Photonics Engineering, Technical University of Denmark, 2800 Kongens Lyngby, DenmarkArtificial intelligence (AI) has been increasingly applied to condition-based maintenance (CBM), a knowledge-based method taking advantage of human expertise and other system knowledge that can serve as an alternative in cases in which machine learning is inapplicable due to a lack of training data. Functional information is seen as the most fundamental and important knowledge in maintenance decision making. This paper first proposes a mechanical functional modeling approach based on a functional modeling and reasoning methodology called multilevel flow modeling (MFM). The approach actually bridges the modeling gap between the mechanical level and the process level, which potentially extends the existing capability of MFM in rule-based diagnostics and prognostics from operation support to maintenance support. Based on this extension, a framework of optimized CBM is proposed, which can be used to diagnose potential mechanical failures from condition monitoring data and predict their future impacts in a qualitative way. More importantly, the framework uses MFM-based reliability-centered maintenance (RCM) to determine the importance of a detected potential failure, which can ensure the cost-effectiveness of CBM by adapting the maintenance requirements to specific operational contexts. This ability cannot be offered by existing CBM methods. An application to a mechanical test apparatus and hypothetical coupling with a process plant are used to demonstrate the proposed framework.https://www.mdpi.com/2079-9292/12/20/4267condition-based maintenancefunctional modelingmechanical functionsfunctional reasoningdiagnosticsprognostics |
| spellingShingle | Mengchu Song Ilmar F. Santos Xinxin Zhang Jing Wu Morten Lind Explicit Representation of Mechanical Functions for Maintenance Decision Support Electronics condition-based maintenance functional modeling mechanical functions functional reasoning diagnostics prognostics |
| title | Explicit Representation of Mechanical Functions for Maintenance Decision Support |
| title_full | Explicit Representation of Mechanical Functions for Maintenance Decision Support |
| title_fullStr | Explicit Representation of Mechanical Functions for Maintenance Decision Support |
| title_full_unstemmed | Explicit Representation of Mechanical Functions for Maintenance Decision Support |
| title_short | Explicit Representation of Mechanical Functions for Maintenance Decision Support |
| title_sort | explicit representation of mechanical functions for maintenance decision support |
| topic | condition-based maintenance functional modeling mechanical functions functional reasoning diagnostics prognostics |
| url | https://www.mdpi.com/2079-9292/12/20/4267 |
| work_keys_str_mv | AT mengchusong explicitrepresentationofmechanicalfunctionsformaintenancedecisionsupport AT ilmarfsantos explicitrepresentationofmechanicalfunctionsformaintenancedecisionsupport AT xinxinzhang explicitrepresentationofmechanicalfunctionsformaintenancedecisionsupport AT jingwu explicitrepresentationofmechanicalfunctionsformaintenancedecisionsupport AT mortenlind explicitrepresentationofmechanicalfunctionsformaintenancedecisionsupport |