Multi-mode Fault Diagnosis Datasets of Gearbox Under Variable Working Conditions
Not on the map. Why?
Abstract (arXiv)
The gearbox is a critical component of electromechanical systems. The occurrence of multiple faults can significantly impact system accuracy and service life. The vibration signal of the gearbox is an effective indicator of its operational status and fault information. However, gearboxes in real industrial settings often operate under variable working conditions, such as varying speeds and loads. It is a significant and challenging research area to complete the gearbox fault diagnosis procedure under varying operating conditions using vibration signals. This data article presents vibration datasets collected from a gearbox exhibiting various fault degrees of severity and fault types, operating under diverse speed and load conditions. These faults are manually implanted into the gears or bearings through precise machining processes, which include health, missing teeth, wear, pitting, root cracks, and broken teeth. Several kinds of actual compound faults are also encompassed. The development of these datasets facilitates testing the effectiveness and reliability of newly developed fault diagnosis methods.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Mar 2024.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2025: 2; 2026: 3.
5 papers.
- 2025Multi-output Classification using a Cross-talk Architecture for Compound Fault Diagnosis of Motors in Partially Labeled Conditioncited by 1
- 2025Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosiscited by 1
- 2026Disentangling Damage from Operational Variability: A Label-Free Self-Supervised Representation Learning Framework for Output-Only Structural Damage Identificationcited by 0
- 2026Asymmetric Adaptation-based Real-time Fault Diagnosis Under Transitional Operating Conditionscited by 0
- 2026When Labels Are Scarce: An Oscillatory State Space Model for Vibration Diagnosiscited by 0
- 2026When Labels Are Scarce: An Oscillatory State Space Model for Vibration Diagnosis23 Sep 2026
- 2026Asymmetric Adaptation-based Real-time Fault Diagnosis Under Transitional Operating Conditions23 May 2026
- 2026Disentangling Damage from Operational Variability: A Label-Free Self-Supervised Representation Learning Framework for Output-Only Structural Damage Identification21 Apr 2026
- 2025Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis21 Jun 2025
- 2025Multi-output Classification using a Cross-talk Architecture for Compound Fault Diagnosis of Motors in Partially Labeled Condition29 May 2025