Publicação
High frequency ultrasonic condition monitoring framework based on edge-computing and telemetry stack approach
| datacite.subject.fos | Engenharia e Tecnologia | |
| dc.contributor.author | Spencer, Geoffrey | |
| dc.contributor.author | Torres, Pedro | |
| dc.contributor.author | Pinto, Vítor H. | |
| dc.contributor.author | Gonçalves, Gil | |
| dc.date.accessioned | 2026-04-15T16:02:45Z | |
| dc.date.available | 2026-04-15T16:02:45Z | |
| dc.date.issued | 2026 | |
| dc.description | The authors would like to thank the SYSTEC-DIGI2 research team for their technical support during the experimental phase. | |
| dc.description.abstract | This paper presents initial developments towards a high-frequency condition monitoring framework designed for Autonomous Mobile Robots (AMRs) in Smart Factory environments. The proposed approach focuses on data acquisition and edge-level processing at the ultrasound range specifically (>20 kHz), using Micro-Electro-Mechanical System (MEMS) sensors. The system integrates real-time data acquisition, embedded fixed-point frequency-domain processing via a 1024-point FFT, and the integration of Industrial Internet-of-Things (IIoT) infrastructure based on the TIG (Telegraf, InfluxDB, and Grafana) stack, for data aggregation and remote visualization. To ensure timing precision at a sampling rate of 160 kHz, a software-based calibration routine is implemented to compensate for microcontroller overhead. Furthermore, the architecture’s alignment with IEEE 1451 principles is discussed to support interoperable and scalable sensor integration. Experimental results validate the reliable acquisition and processing of ultrasonic signals up to 80 kHz using controlled acoustic sources. This work provides a foundational infrastructure for condition-based monitoring, enabling future development of automated anomaly detection for mechanical components, such as bearings, which exhibit early-stage fault signatures in the ultrasonic spectrum. | eng |
| dc.description.sponsorship | The authors acknowledge the Project GreenAuto: Green Innovation for Automotive Industry project, nº C644867037-00000013, investment project nº 54, from the Incentive System to Mobilizing Agendas for Business Innovation, financed by the Recovery and Resilience Plan and by European Funds Next Generation EU. The authors also acknowledge Fundação para a Ciência e a Tecnologia (FCT) for its financial support via the project LA/P/0112/2020 (ARISE). | |
| dc.identifier.citation | SPENCER, Geoffrey [et al.] (2026) - High frequency ultrasonic condition monitoring framework based on edge-computing and telemetry stack approach. Machines. Vol. 14, n.º 3. DOI: 10.3390/machines14030270 | |
| dc.identifier.doi | 10.3390/machines14030270 | |
| dc.identifier.issn | 2075-1702 | |
| dc.identifier.uri | http://hdl.handle.net/10400.11/10842 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI AG | |
| dc.relation.ispartof | Machines | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Industry 4.0 | |
| dc.subject | Smart factory | |
| dc.subject | Condition monitoring | |
| dc.subject | IEEE 1451 | |
| dc.subject | Smart transducer | |
| dc.title | High frequency ultrasonic condition monitoring framework based on edge-computing and telemetry stack approach | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 3 | |
| oaire.citation.title | Machines | |
| oaire.citation.volume | 14 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | BAPTISTA TORRES | |
| person.givenName | PEDRO MIGUEL | |
| person.identifier | K-5331-2015 | |
| person.identifier.ciencia-id | 2711-E707-519C | |
| person.identifier.orcid | 0000-0003-4835-5022 | |
| person.identifier.scopus-author-id | 56261515100 | |
| relation.isAuthorOfPublication | 9d9ad49f-3c45-4a99-be21-7f13965c2628 | |
| relation.isAuthorOfPublication.latestForDiscovery | 9d9ad49f-3c45-4a99-be21-7f13965c2628 |