Rolling bearing fault diagnosis based on DRS frequency spectrum image and improved DQN
Bibliographic record
Abstract
Deep learning may encounter challenges in fault diagnosis, such as exploration capability, long-term planning, and handling non-deterministic factors. The paper introduces a novel fault diagnosis method for rolling bearings combining deep Q-network (DQN) with discrete random separation (DRS) frequency spectrum images. DRS can be used to separate the deterministic components and random components of the fault signal and extract the fault feature very effectively. The improved DQN incorporates the atrous spatial pyramid pooling module for multiscale contextual fault feature extraction, enhancing diagnosis accuracy. Various deep networks, including convolutional neural network, ResNet18, traditional DQN, and the improved DQN, are employed with different frequency spectrum images (power spectral density, cepstrum, and DRS) for diagnosing bearing faults. Simulation results demonstrate that combining DRS frequency spectrum images with the improved DQN enhances fault diagnosis accuracy across diverse conditions. Generalization tests reveal strong capability of the proposed method in handling conditions different from the training data. Validation tests on difficult-to-diagnose fault data from the CWRU-bearing dataset demonstrate commendable performance of the improved DQN, even on difficult datasets. Finally, validation tests using the XJTU–SY bearing dataset reaffirm the excellent performance and robust adaptability of the improved DQN in conjunction with DRS frequency spectrum images.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".