A fast temperature rise identification method based on an adaptive particle filter

Abstract

A fast temperature rise identification method for motorized spindle is presented based on an adaptive particle filter (APF). The advantage of this method is the ability to predict the entire temperature change in a short time. For example, to obtain the whole temperature variation curve of the measured points, it will take about 390 min. However, if APF method is used, the data of the first 24 min can be used to predict the entire temperature change. Temperature signals with multiple variables, long time delay, and nonlinear coupling characteristics as well as the uncertainty of actual measurement of noise in the process making measured temperature fluctuate widely. However, sampled data contain a lot of noise; hence, finding the real temperature range and predicting the future temperature changes using only few data are quite challenging tasks. For this reason, particle filter (PF) method was utilized to get the identification result. Then, the adaptive factor was introduced by adjusting state equations and observation noise variance. Results showed that APF can achieve better identification of temperature rise. Finally, the temperature rise test was verified on the motorized spindle, which utilized PF method and the APF method. Experimental results showed that APF can reduce the recognition time to about 24 min, while the PF takes about 38 min. The effectiveness of the method was verified by using experimental data, and the approach to obtain the minimal recognition time was also explained. In 390 min of measuring time, the root mean square error (RMSE) between estimated and measured temperature is 0.415 °C, and the error between the estimated and measured steady-state temperature is 0.01 °C. The results proved that this method can be used to identify motorized spindle temperature, with high speed and high accuracy.