Figshare
open access
CC BY 4.0
Paper information: Fault Severity Classification in Gearboxes Using Sensor Signals and Artificial Intelligence Approaches: A Systematic Literature Review.
applied-statisticsclassification-modelsdynamicsfailure-severityfeature-selectiongear-failuresmachine-learningvibration-and-vibration-control
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
2026-09-13T09:35:33Z https://api.figshare.com/v2/oai The request includes illegal arguments, is missing required arguments, includes a repeated argument, or values for arguments have an illegal syntax.
Related documents
- Paper information: Fault Severity Classification in Gearboxes Using Sensor Signals and Artificial Intelligence Approaches: A Systematic Literature Review.#191713
- A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids#1122751
- Cost Analysis of a Condition Monitoring System for Journal Bearings in Wind Turbine Gearboxes / Thomas Matthias Decker, Georg Jacobs, Martin Knops, Julian Röder#658411
- Implementation and experimental validation of a hybrid intelligent sliding mode control strategy for induction motor drives.#422939
- Artificial Intelligence in Government Services: A Systematic Literature Review#964494
Record · ID 191686 · SHA-256 5626e47f5b48c408
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive