Log Data Preparation for Predicting Critical Errors Occurrences
| dc.rights.license | open | en_US |
| hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
| dc.contributor.author | LOPEZ, Myriam | |
| hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
| dc.contributor.author | BEURTON AIMAR, Marie
IDREF: 122639839 | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | DIALLO, Abdourahmane Gayo
ORCID: 0000-0002-9799-9484 IDREF: 112800084 | |
| hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
| dc.contributor.author | MAABOUT, Sofian
IDREF: 148141978 | |
| dc.date.accessioned | 2021-08-20T08:55:36Z | |
| dc.date.available | 2021-08-20T08:55:36Z | |
| dc.date.issued | 2021 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/110174 | |
| dc.description.abstractEn | Failure anticipation is one of the key industrial research objectives with the advent of Industry 4.0. This paper presents an approach to predict high importance errors using log data emitted by machine tools. It uses the concept of bag to summarize events provided by remote machines, available within log files. The idea of bag is inspired by the Multiple Instance Learning paradigm. However, our proposal follows a different strategy to label bags, that we wanted as simple as possible. Three main setting parameters are defined to build the training set allowing the model to fine-tune the trade-off between early warning, historic informativeness and forecast accuracy. The effectiveness of the approach is demonstrated using a real industrial application where critical errors can be predicted up to seven days in advance thanks to a classification model. | |
| dc.language.iso | EN | en_US |
| dc.source.title | Advances in Intelligent Systems and Computing | en_US |
| dc.subject.en | Predictive Maintenance (PdM) | |
| dc.subject.en | Machine Learning (ML | |
| dc.subject.en | Classification Log data | |
| dc.subject.en | Data preparation | |
| dc.title.en | Log Data Preparation for Predicting Critical Errors Occurrences | |
| dc.type | Chapitre d'ouvrage | en_US |
| dc.identifier.doi | 10.1007/978-3-030-72651-5_22 | en_US |
| dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | en_US |
| bordeaux.page | 224-233 | en_US |
| bordeaux.volume | 1366 | en_US |
| bordeaux.hal.laboratories | Bordeaux Population Health Research Center (BPH) - UMR 1219 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | INSERM | en_US |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.team | ERIAS | en_US |
| bordeaux.inpress | non | en_US |
| hal.export | false | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Advances%20in%20Intelligent%20Systems%20and%20Computing&rft.date=2021&rft.volume=1366&rft.spage=224-233&rft.epage=224-233&rft.au=LOPEZ,%20Myriam&BEURTON%20AIMAR,%20Marie&DIALLO,%20Abdourahmane%20Gayo&MAABOUT,%20Sofian&rft.genre=unknown |
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