Ultralow Latency ANN–SNN Conversion for Bearing Fault Diagnosis
Bibliographic record
Abstract
Spiking neural networks (SNNs) achieve an impressive performance due to low power consumption and quick inference on neuromorphic hardware. Among various SNN training methods, the ANN-SNN conversion approach can achieve performance levels comparable to those of artificial neural networks (ANNs). However, the spike firing rate of SNNs needs to be aligned with the activation of ANNs over longer time steps, which leads to a performance decline of SNNs at short time steps, limiting their applicability. In response to the challenge, this research proposes an innovative framework for bearing fault diagnosis. The framework first trains an ANN model with a multiscale convolutional attention mechanism (MCNN-AM) that has the capability of feature extraction and noise resistance. Subsequently, the ANN is trained and converted to an SNN using the quantization clip-floor-shift (QCFS) activation function. During the inference phase, an optimization strategy based on residual membrane potential (SRP) is introduced to effectively reduce the SNN response latency while maintaining diagnostic accuracy. The proposed framework enhances the capability of SNNs for diagnosing faults in bearings and enables the deployment of SNNs on compact and mobile platforms.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".