A hybrid, distributed condition monitoring system using MEMS microphones, artificial neural networks, and cloud computing
- Condition monitoring supported with artificial intelligence, cloud computing, and industrial internet of things (IIoT) technologies increases the feasibility of predictive maintenance. However, the cost of traditional sensors, data acquisition systems, and the required information technology expert-knowledge challenge the industry. This paper presents a hybrid condition monitoring system (CMS) architecture consisting of a distributed, low-cost IIoT-sensor solution. The CMS uses micro-electro-mechanical system (MEMS) microphones for data acquisition, edge computing for signal preprocessing, and cloud computing, including artificial neural networks (ANN) for higher-level information processing. The system's feasibility is validated using a testbed for reciprocating linear-motion axes.
| Author of HS Reutlingen | Dorka, Frithjof; Lucke, Dominik |
|---|---|
| URN: | urn:nbn:de:bsz:rt2-opus4-45020 |
| DOI: | https://doi.org/10.1016/j.procir.2023.06.024 |
| ISSN: | 2212-8271 |
| Erschienen in: | Procedia CIRP |
| Publisher: | Elsevier |
| Place of publication: | Amsterdam |
| Document Type: | Journal article |
| Language: | English |
| Publication year: | 2023 |
| Tag: | artificial neural networks; cloud computing; condition monitoring system |
| Volume: | 118 |
| Issue: | 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME ‘22, Italy |
| Page Number: | 5 |
| First Page: | 134 |
| Last Page: | 138 |
| DDC classes: | 670 Industrielle Fertigung |
| Open access?: | Ja |
| Licence (German): |  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |
