Skip to search boxSkip to navigationSkip to main content

Exploring dataset availability and methods for enhanced automated fault detection in induction motors

Research Output: Contribution to journal Article Peer-review

Open access

Abstract

Induction motors are widely used in industrial applications due to their reliability, efficiency, and cost-effectiveness. However, unexpected faults still occur, leading to unplanned downtime and increased operational costs. The effectiveness of fault detection systems is fundamentally dependent on the availability of high-quality and representative datasets. Fault-detection systems are most effective when trained on high-quality and representative datasets. This paper presents a comprehensive evaluation of publicly available induction motor datasets and machine-learning methods that can help detect faults. The study provides a structured dataset taxonomy and identifies key limitations reported in the literature. We investigated datasets acquired through experimental studies and analyzed sensor-based measurements, focusing on vibration, current, and acoustic modalities. Key challenges include the lack of real-world data, limited standardization, selective fault representation, and incomplete metadata. Multiple datasets are lab-based, with variations in size, structure, and sensor placement that limit their generalization to machine learning models. The study further highlights the importance of multi-sensor integration, improved fault labeling, and open-access data sharing to enhance dataset quality and support the development of standardized, comprehensive datasets for reliable induction motor fault detection.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 76990-77013 (24 pages)

Journal (Volume, Issue Number)

IEEE Access (Volume 14)

Publication milestones

  • Accepted/In press - 22/03/2026
  • Published - 03/04/2026

Publication status

Published - 03/04/2026

External Publication IDs

  • Scopus: 105034839768

Access to documents

Publication metrics

Metrics

Download statistics
Download count
4