FPGA-based intelligent bearing fault diagnosis under varying-speed conditions
Bibliographic record
Abstract
Abstract Although machine learning has made remarkable advancements in addressing complex classification tasks for non-stationary signals, this artificial intelligence technique still faces deployment challenges in real-time industrial applications due to substantial computational requirements and prolonged processing times. In this study, a fault diagnosis framework based on Kalman filter-based empirical mode decomposition (KF-EMD) and a back propagation neural network (BPNN) is implemented on an field-programmable gate array (FPGA) for real-time bearing fault diagnosis. First, angular domain resampling is employed to compensate for speed variations. Then, KF-EMD is applied to decompose the resampled signal in the angular domain, generating multiple intrinsic mode functions (IMFs) in parallel. Finally, a lightweight BPNN is utilized to perform real-time fault classification based on features extracted from these IMFs. The proposed method was validated using a bearing fault dataset from the University of Ottawa, achieving a diagnostic accuracy of 99.61%. Real-time diagnostic experiments using pre-stored data on the FPGA demonstrated the effectiveness of the approach in balancing computational efficiency and diagnostic accuracy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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 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".