Systematic Literature Review on AI-Based Gearbox Fault Severity Classification

A systematic literature review on fault severity classification in gearboxes using sensor signals and artificial intelligence. The review includes a critical CASP evaluation, an information extraction matrix, and follows the PRISMA 2020 framework. It was authored by Antonio Pérez-Torres and last updated on 2026-05-17.

Use Cases

• Benchmarking AI approaches for gearbox fault classification based on the reviewed literature corpus.
• Identifying research gaps in sensor-based fault severity assessment using the systematic review methodology.
• Extracting bibliometric trends from the cleaned Web of Science/Scopus data included in the review.

Strengths

• Follows the PRISMA 2020 systematic review framework, ensuring a structured methodology.
• Includes cleaned bibliometric data from Web of Science and Scopus sources.
• Provides associated R scripts for bibliometrix analysis.

Limitations

• Row count and specific data file formats are unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Antonio Pérez-Torres via figshare

Collection Method

Systematic literature review following PRISMA 2020, with data from Web of Science and Scopus.

Freshness

Last updated 2026-05-17 04:43:46; freshness should be verified.