Clustering Application for Condition-Based Maintenance in Time-Varying Processes: A Review Using Latent Dirichlet Allocation
In the field of industrial process monitoring, scholars and practitioners are increasing interest in time-varying processes, where different phases are implemented within an unknown time frame. The measurement of process parameters could inform about the health state of the production assets, or pro...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/2/814 |
| _version_ | 1797496048878354432 |
|---|---|
| author | Elena Quatrini Silvia Colabianchi Francesco Costantino Massimo Tronci |
| author_facet | Elena Quatrini Silvia Colabianchi Francesco Costantino Massimo Tronci |
| author_sort | Elena Quatrini |
| collection | DOAJ |
| description | In the field of industrial process monitoring, scholars and practitioners are increasing interest in time-varying processes, where different phases are implemented within an unknown time frame. The measurement of process parameters could inform about the health state of the production assets, or products, but only if the measured parameters are coupled with the specific phase identification. A combination of values could be common for one phase and uncommon for another phase; thus, the same combination of values shows a high or low probability depending on the specific phase. The automatic identification of the production phase usually relies on clustering techniques. This is largely due to the difficulty of finding training fault data for supervised models. With these two considerations in mind, this contribution proposes the Latent Dirichlet Allocation as a natural language-processing technique for reviewing the topic of clustering applied in time-varying contexts, in the maintenance field. Thus, the paper presents this innovative methodology to analyze this specific research fields, presenting the step-by-step application and its results, with an overview of the theme. |
| first_indexed | 2024-03-10T01:58:10Z |
| format | Article |
| id | doaj.art-bd556305c1b142e9b36524b01405c8fd |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T01:58:10Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-bd556305c1b142e9b36524b01405c8fd2023-11-23T12:53:17ZengMDPI AGApplied Sciences2076-34172022-01-0112281410.3390/app12020814Clustering Application for Condition-Based Maintenance in Time-Varying Processes: A Review Using Latent Dirichlet AllocationElena Quatrini0Silvia Colabianchi1Francesco Costantino2Massimo Tronci3Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, ItalyDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, ItalyDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, ItalyDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, ItalyIn the field of industrial process monitoring, scholars and practitioners are increasing interest in time-varying processes, where different phases are implemented within an unknown time frame. The measurement of process parameters could inform about the health state of the production assets, or products, but only if the measured parameters are coupled with the specific phase identification. A combination of values could be common for one phase and uncommon for another phase; thus, the same combination of values shows a high or low probability depending on the specific phase. The automatic identification of the production phase usually relies on clustering techniques. This is largely due to the difficulty of finding training fault data for supervised models. With these two considerations in mind, this contribution proposes the Latent Dirichlet Allocation as a natural language-processing technique for reviewing the topic of clustering applied in time-varying contexts, in the maintenance field. Thus, the paper presents this innovative methodology to analyze this specific research fields, presenting the step-by-step application and its results, with an overview of the theme.https://www.mdpi.com/2076-3417/12/2/814Latent Dirichlet Allocationnatural language processingcondition-based maintenance |
| spellingShingle | Elena Quatrini Silvia Colabianchi Francesco Costantino Massimo Tronci Clustering Application for Condition-Based Maintenance in Time-Varying Processes: A Review Using Latent Dirichlet Allocation Applied Sciences Latent Dirichlet Allocation natural language processing condition-based maintenance |
| title | Clustering Application for Condition-Based Maintenance in Time-Varying Processes: A Review Using Latent Dirichlet Allocation |
| title_full | Clustering Application for Condition-Based Maintenance in Time-Varying Processes: A Review Using Latent Dirichlet Allocation |
| title_fullStr | Clustering Application for Condition-Based Maintenance in Time-Varying Processes: A Review Using Latent Dirichlet Allocation |
| title_full_unstemmed | Clustering Application for Condition-Based Maintenance in Time-Varying Processes: A Review Using Latent Dirichlet Allocation |
| title_short | Clustering Application for Condition-Based Maintenance in Time-Varying Processes: A Review Using Latent Dirichlet Allocation |
| title_sort | clustering application for condition based maintenance in time varying processes a review using latent dirichlet allocation |
| topic | Latent Dirichlet Allocation natural language processing condition-based maintenance |
| url | https://www.mdpi.com/2076-3417/12/2/814 |
| work_keys_str_mv | AT elenaquatrini clusteringapplicationforconditionbasedmaintenanceintimevaryingprocessesareviewusinglatentdirichletallocation AT silviacolabianchi clusteringapplicationforconditionbasedmaintenanceintimevaryingprocessesareviewusinglatentdirichletallocation AT francescocostantino clusteringapplicationforconditionbasedmaintenanceintimevaryingprocessesareviewusinglatentdirichletallocation AT massimotronci clusteringapplicationforconditionbasedmaintenanceintimevaryingprocessesareviewusinglatentdirichletallocation |