Comparative Analysis of CNN and LSTM for Bearing Fault Mode Classification and Causality Through Representation Analysis
Bibliographic record
Abstract
This study investigates how the clarity of frequency-domain characteristics in vibration signals affects the performance of deep learning models for bearing fault classification. Two datasets were used; these were the CWRU benchmark dataset, which exhibits distinct and easily separable spectral signatures across fault modes, and a custom low-speed bearing dataset in which small defects do not significantly alter the frequency spectrum. To enable a clear and interpretable comparison, simplified CNN and LSTM architectures with a single core layer were deliberately employed. This design choice allows performance differences to be attributed directly to the inherent learning mechanisms of each architecture rather than to model complexity. Representation analysis shows that LSTM-F achieves the highest accuracy when the dataset contains clearly distinguishable spectral patterns, as in the CWRU case. In contrast, CNN-S outperforms both LSTM models in the experimental dataset, where fault-induced frequency characteristics are weak or ambiguous. Additional representation analyses further reveal that LSTM-F relies on consistent frequency-indexed patterns, whereas CNN-S captures more complex time–frequency interactions, making it more robust under low-separability conditions. These findings demonstrate that the optimal deep learning architecture for bearing fault classification depends on the degree of frequency separability in the data. LSTM-F is preferable for severe faults with distinct spectral features, while CNN-S is more effective for minor defects or systems exhibiting complex, weakly discriminative frequency behavior.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".