Details
| Original language | English |
|---|---|
| Title of host publication | IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning |
| Subtitle of host publication | Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers |
| Editors | Joao Gama, Sepideh Pashami, Albert Bifet, Moamar Sayed-Mouchawe, Holger Fröning, Franz Pernkopf, Gregor Schiele, Michaela Blott |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 106–118 |
| Number of pages | 13 |
| Edition | 1 |
| ISBN (electronic) | 978-3-030-66770-2 |
| ISBN (print) | 978-3-030-66769-6 |
| Publication status | Published - 10 Jan 2021 |
| Externally published | Yes |
| Event | 2nd International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and 1st International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 - Ghent, Belgium Duration: 14 Sept 2020 → 18 Sept 2020 |
Publication series
| Name | Communications in Computer and Information Science |
|---|---|
| Volume | 1325 |
| ISSN (Print) | 1865-0929 |
| ISSN (electronic) | 1865-0937 |
Abstract
Automated machine learning (AutoML) deals with the automatic composition and configuration of machine learning pipelines, including the selection and parametrization of preprocessors and learning algorithms. While recent work in this area has shown impressive results, existing approaches are essentially limited to standard problem classes such as classification and regression. In parallel, research in the field of predictive maintenance, particularly remaining useful lifetime (RUL) estimation, has received increasing attention, due to its practical relevance and potential to reduce unplanned downtime in industrial plants. However, applying existing AutoML methods to RUL estimation is non-trivial, as in this domain, one has to deal with varying-length multivariate time series data. Furthermore, the data often directly originates from real-world scenarios or simulations, and hence requires extensive preprocessing. In this work, we present ML-Plan-RUL, an adaptation of the AutoML tool ML-Plan to the problem of RUL estimation. To the best of our knowledge, it is the first tool specifically tailored towards automated RUL estimation, combining feature engineering, algorithm selection, and hyperparameter optimization into an end-to-end approach. First promising experimental results demonstrate the efficacy of ML-Plan-RUL.
Keywords
- AutoML, Predictive maintenance, Remaining useful lifetime
ASJC Scopus subject areas
- Mathematics(all)
- General Mathematics
- Computer Science(all)
- General Computer Science
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IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning: Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers. ed. / Joao Gama; Sepideh Pashami; Albert Bifet; Moamar Sayed-Mouchawe; Holger Fröning; Franz Pernkopf; Gregor Schiele; Michaela Blott. 1. ed. Springer Science and Business Media Deutschland GmbH, 2021. p. 106–118 (Communications in Computer and Information Science; Vol. 1325).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - AutoML for Predictive Maintenance
T2 - 2nd International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and 1st International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020
AU - Tornede, Tanja
AU - Tornede, Alexander
AU - Wever, Marcel
AU - Mohr, Felix
AU - Hüllermeier, Eyke
N1 - Publisher Copyright: © 2020, Springer Nature Switzerland AG.
PY - 2021/1/10
Y1 - 2021/1/10
N2 - Automated machine learning (AutoML) deals with the automatic composition and configuration of machine learning pipelines, including the selection and parametrization of preprocessors and learning algorithms. While recent work in this area has shown impressive results, existing approaches are essentially limited to standard problem classes such as classification and regression. In parallel, research in the field of predictive maintenance, particularly remaining useful lifetime (RUL) estimation, has received increasing attention, due to its practical relevance and potential to reduce unplanned downtime in industrial plants. However, applying existing AutoML methods to RUL estimation is non-trivial, as in this domain, one has to deal with varying-length multivariate time series data. Furthermore, the data often directly originates from real-world scenarios or simulations, and hence requires extensive preprocessing. In this work, we present ML-Plan-RUL, an adaptation of the AutoML tool ML-Plan to the problem of RUL estimation. To the best of our knowledge, it is the first tool specifically tailored towards automated RUL estimation, combining feature engineering, algorithm selection, and hyperparameter optimization into an end-to-end approach. First promising experimental results demonstrate the efficacy of ML-Plan-RUL.
AB - Automated machine learning (AutoML) deals with the automatic composition and configuration of machine learning pipelines, including the selection and parametrization of preprocessors and learning algorithms. While recent work in this area has shown impressive results, existing approaches are essentially limited to standard problem classes such as classification and regression. In parallel, research in the field of predictive maintenance, particularly remaining useful lifetime (RUL) estimation, has received increasing attention, due to its practical relevance and potential to reduce unplanned downtime in industrial plants. However, applying existing AutoML methods to RUL estimation is non-trivial, as in this domain, one has to deal with varying-length multivariate time series data. Furthermore, the data often directly originates from real-world scenarios or simulations, and hence requires extensive preprocessing. In this work, we present ML-Plan-RUL, an adaptation of the AutoML tool ML-Plan to the problem of RUL estimation. To the best of our knowledge, it is the first tool specifically tailored towards automated RUL estimation, combining feature engineering, algorithm selection, and hyperparameter optimization into an end-to-end approach. First promising experimental results demonstrate the efficacy of ML-Plan-RUL.
KW - AutoML
KW - Predictive maintenance
KW - Remaining useful lifetime
UR - http://www.scopus.com/inward/record.url?scp=85101581175&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-66770-2_8
DO - 10.1007/978-3-030-66770-2_8
M3 - Conference contribution
AN - SCOPUS:85101581175
SN - 978-3-030-66769-6
T3 - Communications in Computer and Information Science
SP - 106
EP - 118
BT - IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning
A2 - Gama, Joao
A2 - Pashami, Sepideh
A2 - Bifet, Albert
A2 - Sayed-Mouchawe, Moamar
A2 - Fröning, Holger
A2 - Pernkopf, Franz
A2 - Schiele, Gregor
A2 - Blott, Michaela
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 14 September 2020 through 18 September 2020
ER -