A method for implementation of machine learning solutions for predictive maintenance in small and medium sized enterprises
- In recent years, machine learning algorithms have made a huge development in performance and applicability in industry and especially maintenance. Their application enables predictive maintenance and thus offers efficiency increases. However, a successful implementation of such solutions still requires high effort in data preparation to obtain the right information, interdisciplinarity in teams as well as a good communication to employees. Here, small and medium sized enterprises (SME) often lack in experience, competence and capacity. This paper presents a systematic and practice-oriented method for an implementation of machine learning solutions for predictive maintenance in SME, which has already been validated.
| Author of HS Reutlingen | Welte, Rebecca; Estler, Manfred; Lucke, Dominik |
|---|---|
| URN: | urn:nbn:de:bsz:rt2-opus4-28125 |
| DOI: | https://doi.org/10.1016/j.procir.2020.04.052 |
| ISSN: | 2212-8271 |
| Published in: | Procedia CIRP |
| Publisher: | Elsevier |
| Place of publication: | Amsterdam |
| Document Type: | Journal article |
| Language: | English |
| Publication year: | 2020 |
| Tag: | implementation method; machine learning; predictive maintenance |
| Volume: | 93 |
| Page Number: | 6 |
| First Page: | 909 |
| Last Page: | 914 |
| DDC classes: | 670 Industrielle und handwerkliche Fertigung |
| Open access?: | Ja |
| Licence (German): |  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |
