Condition monitoring in the cloud
| dc.contributor.author | Uhlmann, Eckhart | |
| dc.contributor.author | Laghmouch, Abdelhakim | |
| dc.contributor.author | Hohwieler, Eckhard | |
| dc.contributor.author | Geisert, Claudio | |
| dc.date.accessioned | 2018-05-28T12:11:09Z | |
| dc.date.available | 2018-05-28T12:11:09Z | |
| dc.date.issued | 2015 | |
| dc.description.abstract | Due to the very high demands on availability and efficiency of production systems and industrial systems, condition-based maintenance is becoming increasingly important. The use of condition monitoring approaches to increase the machine availability and reduce the maintenance costs, as well as to enhance the process quality, has increased over the last years. The installation of industrial sensors for condition monitoring reasons is complex and cost-intensive. Moreover, the condition monitoring systems available on the market are application specific and expensive. The aim of this paper is to present the concept of a wireless sensor network using Micro-Electro-Mechanical Systems – MEMS sensors and Raspberry Pi 2 for data acquisition and signal processing and classification. Moreover, its use for condition monitoring applications and the selected and implemented algorithm will be introduced. This concept realized by Fraunhofer Institute for Production Systems and Design Technology IPK, can be used to detect faults in wear-susceptible rotating components in production systems. It can be easily adapted to different specific applications because of decentralized data preprocessing on the sensor nodes and pool of data and services in the cloud. A concrete example for an industrial application of this concept will be represented. This will include the visualization of results which were achieved. Finally, the evaluation and testing of this concept including. implemented algorithms on an axis test rig at different operation parameters will be illustrated. | en |
| dc.identifier.issn | 2212-8271 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/7874 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-7034 | |
| dc.language.iso | en | en |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
| dc.subject.ddc | 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten | de |
| dc.subject.other | condition monitoring | en |
| dc.subject.other | algorithms | en |
| dc.subject.other | wireless sensor network | en |
| dc.subject.other | classification | en |
| dc.title | Condition monitoring in the cloud | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.doi | 10.1016/j.procir.2015.08.075 | en |
| dcterms.bibliographicCitation.journaltitle | Procedia CIRP | en |
| dcterms.bibliographicCitation.originalpublishername | Elsevier | en |
| dcterms.bibliographicCitation.originalpublisherplace | Amsterdam | en |
| dcterms.bibliographicCitation.pageend | 57 | en |
| dcterms.bibliographicCitation.pagestart | 53 | en |
| dcterms.bibliographicCitation.volume | 38 | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 5 Verkehrs- und Maschinensysteme::Inst. Werkzeugmaschinen und Fabrikbetrieb::FG Montagetechnik und Fabrikbetrieb | de |
| tub.affiliation.faculty | Fak. 5 Verkehrs- und Maschinensysteme | de |
| tub.affiliation.group | FG Montagetechnik und Fabrikbetrieb | de |
| tub.affiliation.institute | Inst. Werkzeugmaschinen und Fabrikbetrieb | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |