Details
| Original language | English |
|---|---|
| Title of host publication | Exploring Service Science - 9th International Conference, IESS 2018, Proceedings |
| Subtitle of host publication | 9th International Conference, IESS 2018, Karlsruhe, Germany, September 19–21, 2018, Proceedings |
| Editors | Gerhard Satzger, Niklas Kühl, Peter Hottum, Mohamed Zaki, Lia Patrício |
| Place of Publication | Cham |
| Pages | 261-273 |
| Number of pages | 13 |
| ISBN (electronic) | 978-3-030-00713-3 |
| Publication status | Published - 2018 |
Publication series
| Name | Lecture Notes in Business Information Processing |
|---|---|
| Volume | 331 |
| ISSN (Print) | 1865-1348 |
Abstract
To ensure availability of industrial machines and reducing breakdown times, a machine monitoring can be an essential help. Unexpected machine downtimes are typically accompanied by high costs. Machine builders as well as component suppliers can use their detailed knowledge about their products to counteract this. One possibility to face the challenge is to offer a product-service system with machine monitoring services to their customers. An implementation approach for such a machine monitoring service is presented in this article. In contrast to previous research, we focus on the integration and interaction of machine learning tools and human domain experts, e.g. for an early anomaly detection and fault classification. First, Long Short-Term Memory Neural Networks are trained and applied to identify unusual behavior in operation time series data of a machine. We describe first results of the implementation of this anomaly detection. Second, domain experts are confronted with related monitoring data, e.g. temperature, vibration, video, audio etc., from different sources to assess and classify anomaly types. With an increasing knowledge base, a classifier module automatically suggests possible causes for an anomaly automatically in advance to support machine operators in the anomaly identification process. Feedback loops ensure continuous learning of the anomaly detector and classifier modules. Hence, we combine the knowledge of machine builders/component suppliers with application specific experience of the customers in the business value stream network.
Keywords
- Hybrid learning, Long Short-Term Memory Neural Networks, Machine monitoring, Product-service systems
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Management Information Systems
- Engineering(all)
- Control and Systems Engineering
- Business, Management and Accounting(all)
- Business and International Management
- Computer Science(all)
- Information Systems
- Mathematics(all)
- Modelling and Simulation
- Decision Sciences(all)
- Information Systems and Management
Cite this
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Exploring Service Science - 9th International Conference, IESS 2018, Proceedings: 9th International Conference, IESS 2018, Karlsruhe, Germany, September 19–21, 2018, Proceedings. ed. / Gerhard Satzger; Niklas Kühl; Peter Hottum; Mohamed Zaki; Lia Patrício. Cham, 2018. p. 261-273 (Lecture Notes in Business Information Processing; Vol. 331).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Combining Machine Learning and Domain Experience - A Hybrid-Learning Monitor Approach for Industrial Machines.
AU - Olivotti, Daniel
AU - Passlick, Jens
AU - Axjonow, Alexander
AU - Eilers, Dennis
AU - Breitner, Michael H.
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions. Publisher Copyright: © 2018, Springer Nature Switzerland AG. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - To ensure availability of industrial machines and reducing breakdown times, a machine monitoring can be an essential help. Unexpected machine downtimes are typically accompanied by high costs. Machine builders as well as component suppliers can use their detailed knowledge about their products to counteract this. One possibility to face the challenge is to offer a product-service system with machine monitoring services to their customers. An implementation approach for such a machine monitoring service is presented in this article. In contrast to previous research, we focus on the integration and interaction of machine learning tools and human domain experts, e.g. for an early anomaly detection and fault classification. First, Long Short-Term Memory Neural Networks are trained and applied to identify unusual behavior in operation time series data of a machine. We describe first results of the implementation of this anomaly detection. Second, domain experts are confronted with related monitoring data, e.g. temperature, vibration, video, audio etc., from different sources to assess and classify anomaly types. With an increasing knowledge base, a classifier module automatically suggests possible causes for an anomaly automatically in advance to support machine operators in the anomaly identification process. Feedback loops ensure continuous learning of the anomaly detector and classifier modules. Hence, we combine the knowledge of machine builders/component suppliers with application specific experience of the customers in the business value stream network.
AB - To ensure availability of industrial machines and reducing breakdown times, a machine monitoring can be an essential help. Unexpected machine downtimes are typically accompanied by high costs. Machine builders as well as component suppliers can use their detailed knowledge about their products to counteract this. One possibility to face the challenge is to offer a product-service system with machine monitoring services to their customers. An implementation approach for such a machine monitoring service is presented in this article. In contrast to previous research, we focus on the integration and interaction of machine learning tools and human domain experts, e.g. for an early anomaly detection and fault classification. First, Long Short-Term Memory Neural Networks are trained and applied to identify unusual behavior in operation time series data of a machine. We describe first results of the implementation of this anomaly detection. Second, domain experts are confronted with related monitoring data, e.g. temperature, vibration, video, audio etc., from different sources to assess and classify anomaly types. With an increasing knowledge base, a classifier module automatically suggests possible causes for an anomaly automatically in advance to support machine operators in the anomaly identification process. Feedback loops ensure continuous learning of the anomaly detector and classifier modules. Hence, we combine the knowledge of machine builders/component suppliers with application specific experience of the customers in the business value stream network.
KW - Hybrid learning
KW - Long Short-Term Memory Neural Networks
KW - Machine monitoring
KW - Product-service systems
UR - http://www.scopus.com/inward/record.url?scp=85053871566&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00713-3_20
DO - 10.1007/978-3-030-00713-3_20
M3 - Conference contribution
SN - 978-3-030-00712-6
T3 - Lecture Notes in Business Information Processing
SP - 261
EP - 273
BT - Exploring Service Science - 9th International Conference, IESS 2018, Proceedings
A2 - Satzger, Gerhard
A2 - Kühl, Niklas
A2 - Hottum, Peter
A2 - Zaki, Mohamed
A2 - Patrício, Lia
CY - Cham
ER -