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.

Clustering application for condition-based maintenance in time-varying processes: a review using latent dirichlet allocation / Quatrini, E.; Colabianchi, S.; Costantino, F.; Tronci, M.. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 12:2(2022), pp. 1-18. [10.3390/app12020814]

Clustering application for condition-based maintenance in time-varying processes: a review using latent dirichlet allocation

Quatrini E.;Colabianchi S.;Costantino F.
;
Tronci M.
2022

Abstract

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.
File allegati a questo prodotto
File Dimensione Formato  
Quatrini_Clustering-application_2022.pdf

accesso aperto

Note: https://doi.org/10.3390/app12020814
Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Creative commons
Dimensione 2.36 MB
Formato Adobe PDF
2.36 MB Adobe PDF Visualizza/Apri PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1605538
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact