ICPHM’23 Benchmark Vibration Dataset Applicable in Machine Learning for Systems’ Health Monitoring
Bibliographic record
Abstract
Vibration signal analysis is an effective tool for fault diagnosis in industrial/manufacturing machinery. Gearboxes are a fundamental component of many industrial machines, and their failure can cause significant downtime, production losses, and safety hazards. Analyzing vibration signals makes it possible to detect, classify, and diagnose faults in gearboxes, enabling timely maintenance and preventing catastrophic failures. Vibration signals are sensitive to changes in the operating conditions and internal components of gearboxes, making them a reliable indicator of potential faults. This paper introduces a new vibration signal data set, referred to as VibraFault, which has been the focus of the ICPHM23 data challenge. The dataset contains vibration signals acquired from a test rig consisting of a driving motor, a two-stage planetary gearbox, a two-stage parallel gearbox, and a magnetic brake. The experiments include various operating conditions and focus on common sun gear faults on the planetary gearbox, such as surface wear, chipped, crack, and tooth-missing. For each operating condition, normal and fault vibration signals have been recorded at a sampling frequency of 10 kHz. Vibration signals have been collected in three directions to facilitate more comprehensive research studies on mapping between different types of faults and the system’s vibration response. The dataset has the potential to promote research in fault diagnosis, particularly in the development of advanced solutions based on Machine Learning (ML) and Deep Neural Networks (DNN).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".