Instantaneous Angular Speed Tracking of Rotating Machines Using a Bayesian Framework
Bibliographic record
Abstract
Abstract Accurate estimation of instantaneous angular speed is crucial for the condition monitoring and diagnosis of rotating machinery. Traditional methods often struggle in noisy, non-stationary, and multi-interfering source environments. This paper proposes a Bayesian framework for tracking instantaneous angular speed from vibration measurements, offering enhanced precision, robustness, and reliability. Our approach models the instantaneous angular speed as a hidden Markov process. The likelihood is derived from the observed spectrogram and the kinematics of the rotating machine, taking into account harmonics. A prior distribution is introduced to leverage the frequency’s continuity and incorporate domain-specific knowledge. By combining the prior and likelihood using Bayes’ theorem, we obtain the posterior distribution, which represents the probability of the instantaneous angular speed trajectory given the observed data. The Bayesian estimator is determined through Viterbi dynamic programming algorithm, yielding the most probable sequence of speed states even in the presence of significant noise. This approach effectively handles non-stationary signals and can adapt to varying operating conditions. Simulation results demonstrate the superior accuracy and robustness of our approach compared to state-of-the-art methods, especially under harsh conditions. This innovative methodology has significant potential for applications in predictive maintenance, fault detection, and improving the overall operational efficiency of rotating machinery in various industrial settings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".