Model for the positional accuracy degradation of NC rotary tables based on the hidden Markov model and optimized particle filtering

A novel prediction approach for NC rotary tables was proposed based on the hidden Markov model(HMM) and the particle filtering(PF) to estimate the degradation trend of the replicated positional accuracy. The initial parameter of particle filtering was optimized by the particle swarm optimization(PSO). The vibration signal was selected as the data for research, which was obtained from an accelerated accuracy degradation test of a NC rotary table. The original signal was denoised and reconstructed by an ensemble empirical mode decomposition and principal component analysis. Then, a HMM was trained by an observation matrix which was composed of the statistical characteristic values, and the diagnosis of early positional accuracy degradation and the health status indicators of the accuracy were obtained. Finally, the degradation trend model of the positional accuracy was established by the particle filtering, and the residual accuracy life was calculated. When fiftieth sets of data were used as the starting point of prediction, the predicted residual life is 21, and the actual measurement result is 17, which are close to each other. Comparing the results of model calculations and experimental measurements, it is shown that the approach is efficient to estimate the degradation trend of the positional accuracy and the residual accuracy life.