Bearing fault diagnosis using normalized diagnostic feature-gram and convolutional neural network
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Bibliographic record
Abstract
Abstract Accurate fault diagnosis is vital for modern maintenance strategies to improve machinery reliability and efficiency. Automated predictive tools, such as deep learning, are gaining more attention as the need for more general and robust diagnosis algorithms is crucial. In this work, a rotational-speed-independent diagnosis algorithm based on using a novel 2D color-coded map as the input to a deep artificial neural network is proposed. The 2D map is named normalized diagnostic feature-gram (NDFgram). The proposed algorithm is applied for bearing fault diagnosis to investigate its effectiveness. For that purpose, the bearing vibration signals are processed first to obtain the bi-frequency spectral coherence (SCoh) data. Secondly, diagnostic features (DFs) are calculated at specific cyclic frequencies owing to bearing faults by integrating the obtained SCoh data over the spectral frequency domain using a center frequency and frequency range. The calculated DFs are represented by a 2D map against the center frequency and frequency resolution. The maps from different fault features are stacked together to form the diagnostic patterns. Thirdly, a pretrained convolutional neural network (CNN) is applied to learn the feature pattern and diagnose the bearing faults. The CNN is trained using fixed-speed data and then it is applied to diagnose faults in the test data recorded at the same speed. Then, it is also tested using variable-speed data and data of another ball bearing type in order to show the independency on the rotational speed and ball bearing type in practice. The results show a 100% success rate for the constant-speed tests and 98.16% accuracy for the variable-speed testing dataset. The accuracy of diagnosing the faults of the second type of ball bearing is 98.56%. The diagnosis accuracy of the proposed method is still high even when a white noise is artificially added to the signals in the noise insusceptibility test. Comparison with other approaches that use different input features to the CNN shows that the proposed is superior in terms of diagnosis accuracy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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