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Digital Twin-based framework for real-time fault diagnosis and prognosis of industrial robots using unsupervised learning and real experimental data
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| Published in: | International Journal of Data Science and Analytics |
|---|---|
| Format: | Online Article RSS Article |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1868553683473530881 |
|---|---|
| collection | WordPress RSS FRELIP Feed Integration |
| container_title | International Journal of Data Science and Analytics |
| description | |
| discipline_display | Big data and data science |
| discipline_facet | Big data and data science |
| format | Online Article RSS Article |
| genre | Journal Article |
| id | rss_article:82881 |
| institution | FRELIP |
| journal_source_facet | International Journal of Data Science and Analytics |
| last_indexed | 2026-06-20T21:40:56.151Z |
| publishDate | 2026 |
| publishDateSort | 2026 |
| record_format | rss_article |
| spellingShingle | Digital Twin-based framework for real-time fault diagnosis and prognosis of industrial robots using unsupervised learning and real experimental data Big data and data science General Big data and data science |
| sub_discipline_display | General |
| sub_discipline_facet | General |
| subject_display | Big data and data science General Big data and data science |
| subject_facet | Big data and data science General Big data and data science |
| title | Digital Twin-based framework for real-time fault diagnosis and prognosis of industrial robots using unsupervised learning and real experimental data |
| title_alt | Marco basado en gemelos digitales para el diagnóstico y pronóstico de fallos en tiempo real de robots industriales mediante aprendizaje no supervisado y datos experimentales reales Cadre basé sur le jumeau numérique pour le diagnostic et le pronostic en temps réel des défauts des robots industriels utilisant l'apprentissage non supervisé et des données expérimentales réelles Estrutura baseada em Gêmeo Digital para diagnóstico e prognóstico de falhas em tempo real de robôs industriais usando aprendizado não supervisionado e dados experimentais reais |
| title_auth | Digital Twin-based framework for real-time fault diagnosis and prognosis of industrial robots using unsupervised learning and real experimental data |
| title_es_txt | Marco basado en gemelos digitales para el diagnóstico y pronóstico de fallos en tiempo real de robots industriales mediante aprendizaje no supervisado y datos experimentales reales |
| title_fr_txt | Cadre basé sur le jumeau numérique pour le diagnostic et le pronostic en temps réel des défauts des robots industriels utilisant l'apprentissage non supervisé et des données expérimentales réelles |
| title_full | Digital Twin-based framework for real-time fault diagnosis and prognosis of industrial robots using unsupervised learning and real experimental data |
| title_fullStr | Digital Twin-based framework for real-time fault diagnosis and prognosis of industrial robots using unsupervised learning and real experimental data |
| title_full_unstemmed | Digital Twin-based framework for real-time fault diagnosis and prognosis of industrial robots using unsupervised learning and real experimental data |
| title_pt_txt | Estrutura baseada em Gêmeo Digital para diagnóstico e prognóstico de falhas em tempo real de robôs industriais usando aprendizado não supervisionado e dados experimentais reais |
| title_short | Digital Twin-based framework for real-time fault diagnosis and prognosis of industrial robots using unsupervised learning and real experimental data |
| title_sort | digital twin-based framework for real-time fault diagnosis and prognosis of industrial robots using unsupervised learning and real experimental data |
| topic | Big data and data science General Big data and data science |
| url | https://link.springer.com/article/10.1007/s41060-026-01164-z |