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An Adaptive Bayesian Harmonic-Comb Reconstruction Method for Spindle Error Motion Testing
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DOI：10.1016/j.precisioneng.2026.04.020.png)
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Abstract
En 中文
• Propose an adaptive Bayesian harmonic-comb reconstruction (ABHC) method for spindle error motion separation. • Achieve sub-bin spindle fundamental frequency estimation via windowing and parabolic spectral-line interpolation. • Use group-wise sparse Bayesian modeling to automatically select effective harmonics and separate synchronous/asynchronous errors. • Develop an integrated spindle error analysis system and validate it against a commercial Lion SEA9 instrument over 1,000–15,000 r/min. • Conduct a rigorous uncertainty analysis to quantify the precision and reliability of the separated error motion results.
Keywords:
Adaptive Bayesian Harmonic-Comb Reconstruction
Spindle Error Motion Separation
Sub-bin Fundamental Frequency Estimation
Sparse Bayesian Modeling
Uncertainty Analysis
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