Exploring Rolling Element Bearing Data Collection and Algorithm Hyperparameters for Machine Learning-Based Fault Diagnosis
Why this work is in the frame
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Bibliographic record
Abstract
This paper explores rolling element bearing data collection and hyperparameter tuning for machine learning-based fault diagnosis to aid in the development of modern condition monitoring systems. The integration of industrial internet of things (IIoT) products and cloud databases has led to an increased interest in utilizing artificial intelligence (AI) models, including artificial neural networks (ANNs) and convolutional neural networks (CNNs), to diagnose machine faults. However, the development of AI methodologies in smart monitoring is hindered by a lack of publicly available industry data, as well as limitations involved in the collection and storage of large high-dimensional datasets. Combining machine learning (ML) methods, such as traditional learning (TL), deep learning (DL), and bearing signature theory, will allow for a better understanding of data collection and hyperparameter tuning. Moreover, considering how high-dimensional datasets for rolling element bearing fault diagnosis affect ML algorithms has yet to be explored in the literature, providing little robustness for analysis. Concerns around the way data has been collected and used historically for both TL and DL are raised. Therefore, recommendations for data collection specifically suited to TL and DL methods for rolling element bearing fault diagnosis are proposed by analyzing existing lab-based datasets. The recommendations proposed combine knowledge of these methodologies to aid in selecting an appropriate sampling rate, as well as the ideal number of samples, stride, duration of each sample, and resolution for rolling element bearing fault diagnosis. The goal is to increase efficiency and reduce setup and collection time when selecting the design parameters for creating new rolling element bearing datasets. To achieve this, the study applied a structured approach with the use of multiple datasets to determine a threshold accuracy of 95% for fault diagnosis. Furthermore, the results of this study will help IIoT companies re-evaluate the constraints imposed by the limited data storage and transmission of their devices when used for ML. This paper will also help improve the efficiency and effectiveness of AI methodologies in smart monitoring systems by establishing data collection recommendations. This work will hopefully motivate the vast collection of open-access data that can be used by researchers to further develop ML-based methods for rolling element fault diagnosis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it