Repetitive accuracy degradation of numerical control rotary table based on hidden Markov model and improved particle filtering

A novel prediction approach based on the hidden Markov model and improved particle filtering for numerical control rotary table was proposed to estimate the degradation trend of repetitive positioning accuracy. Here, the resampling algorithm of the particle filter is improved and data-driven methods are introduced for the first time to study accuracy. The vibration signal obtained from an accelerated accuracy degradation test was selected as the test data. First, the original signal was de-noised and reconstructed by ensemble empirical mode decomposition-principal component analysis. Second, a hidden Markov model was trained by an observation matrix, which was composed of statistical characteristic values. Then, an early diagnosis of repetitive positioning accuracy degradation was obtained and the health status indicator of accuracy was built. Finally, the degradation trend model of repetitive positioning accuracy was established by improved particle filtering, and the residual accuracy life can also be calculated. With model calculations and experimental measurements, the results show that the approach is effective for numerical control rotary tables to estimate the degradation trend of repetitive positioning accuracy and residual accuracy life.