An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance Article Swipe
Javier Fernandez-Anakabe
,
Ekhi Zugasti
,
Urko Zurutuza Ortega
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1912.00662
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1912.00662
Attribute Oriented Induction (AOI) is a data mining algorithm used for extracting knowledge of relational data, taking into account expert knowledge. It is a clustering algorithm that works by transforming the values of the attributes and converting an instance into others that are more generic or ambiguous. In this way, it seeks similarities between elements to generate data groupings. AOI was initially conceived as an algorithm for knowledge discovery in databases, but over the years it has been applied to other areas such as spatial patterns, intrusion detection or strategy making. In this paper, AOI has been extended to the field of Predictive Maintenance. The objective is to demonstrate that combining expert knowledge and data collected from the machine can provide good results in the Predictive Maintenance of industrial assets. To this end we adapted the algorithm and used an LSTM approach to perform both the Anomaly Detection (AD) and the Remaining Useful Life (RUL). The results obtained confirm the validity of the proposal, as the methodology was able to detect anomalies, and calculate the RUL until breakage with considerable degree of accuracy.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1912.00662
- https://arxiv.org/pdf/1912.00662
- OA Status
- green
- Cited By
- 3
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2991301091
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- OpenAlex ID
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https://openalex.org/W2991301091Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1912.00662Digital Object Identifier
- Title
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An Attribute Oriented Induction based Methodology for Data Driven Predictive MaintenanceWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2019Year of publication
- Publication date
-
2019-12-02Full publication date if available
- Authors
-
Javier Fernandez-Anakabe, Ekhi Zugasti, Urko Zurutuza OrtegaList of authors in order
- Landing page
-
https://arxiv.org/abs/1912.00662Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1912.00662Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1912.00662Direct OA link when available
- Concepts
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Data mining, Cluster analysis, Computer science, Knowledge extraction, Field (mathematics), Predictive maintenance, Anomaly detection, Machine learning, Artificial intelligence, Engineering, Reliability engineering, Mathematics, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2022: 1, 2020: 2Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.areas | 81 |
| abstract_inverted_index.data, | 15 |
| abstract_inverted_index.field | 100 |
| abstract_inverted_index.other | 80 |
| abstract_inverted_index.seeks | 51 |
| abstract_inverted_index.until | 176 |
| abstract_inverted_index.works | 27 |
| abstract_inverted_index.years | 74 |
| abstract_inverted_index.(RUL). | 154 |
| abstract_inverted_index.Useful | 152 |
| abstract_inverted_index.degree | 180 |
| abstract_inverted_index.detect | 170 |
| abstract_inverted_index.expert | 19, 111 |
| abstract_inverted_index.mining | 7 |
| abstract_inverted_index.others | 40 |
| abstract_inverted_index.paper, | 93 |
| abstract_inverted_index.taking | 16 |
| abstract_inverted_index.values | 31 |
| abstract_inverted_index.Anomaly | 146 |
| abstract_inverted_index.account | 18 |
| abstract_inverted_index.adapted | 134 |
| abstract_inverted_index.applied | 78 |
| abstract_inverted_index.assets. | 129 |
| abstract_inverted_index.between | 53 |
| abstract_inverted_index.confirm | 158 |
| abstract_inverted_index.generic | 44 |
| abstract_inverted_index.machine | 118 |
| abstract_inverted_index.making. | 90 |
| abstract_inverted_index.perform | 143 |
| abstract_inverted_index.provide | 120 |
| abstract_inverted_index.results | 122, 156 |
| abstract_inverted_index.spatial | 84 |
| abstract_inverted_index.Oriented | 1 |
| abstract_inverted_index.approach | 141 |
| abstract_inverted_index.breakage | 177 |
| abstract_inverted_index.elements | 54 |
| abstract_inverted_index.extended | 97 |
| abstract_inverted_index.generate | 56 |
| abstract_inverted_index.instance | 38 |
| abstract_inverted_index.obtained | 157 |
| abstract_inverted_index.strategy | 89 |
| abstract_inverted_index.validity | 160 |
| abstract_inverted_index.Attribute | 0 |
| abstract_inverted_index.Detection | 147 |
| abstract_inverted_index.Induction | 2 |
| abstract_inverted_index.Remaining | 151 |
| abstract_inverted_index.accuracy. | 182 |
| abstract_inverted_index.algorithm | 8, 25, 65, 136 |
| abstract_inverted_index.calculate | 173 |
| abstract_inverted_index.collected | 115 |
| abstract_inverted_index.combining | 110 |
| abstract_inverted_index.conceived | 62 |
| abstract_inverted_index.detection | 87 |
| abstract_inverted_index.discovery | 68 |
| abstract_inverted_index.initially | 61 |
| abstract_inverted_index.intrusion | 86 |
| abstract_inverted_index.knowledge | 12, 67, 112 |
| abstract_inverted_index.objective | 105 |
| abstract_inverted_index.patterns, | 85 |
| abstract_inverted_index.proposal, | 163 |
| abstract_inverted_index.Predictive | 102, 125 |
| abstract_inverted_index.ambiguous. | 46 |
| abstract_inverted_index.anomalies, | 171 |
| abstract_inverted_index.attributes | 34 |
| abstract_inverted_index.clustering | 24 |
| abstract_inverted_index.converting | 36 |
| abstract_inverted_index.databases, | 70 |
| abstract_inverted_index.extracting | 11 |
| abstract_inverted_index.groupings. | 58 |
| abstract_inverted_index.industrial | 128 |
| abstract_inverted_index.knowledge. | 20 |
| abstract_inverted_index.relational | 14 |
| abstract_inverted_index.Maintenance | 126 |
| abstract_inverted_index.demonstrate | 108 |
| abstract_inverted_index.methodology | 166 |
| abstract_inverted_index.Maintenance. | 103 |
| abstract_inverted_index.considerable | 179 |
| abstract_inverted_index.similarities | 52 |
| abstract_inverted_index.transforming | 29 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |