Obtaining more appropriate temperature sensor locations for thermal error modeling: reduction, classification, and selection

Obtaining appropriate temperature sensor locations is crucial for data-driven thermal error modeling. The pseudo-correlation and variable ranking will cause inappropriate sensor selection results. In this paper, a three-step sensor selection strategy based on the detrended cross-correlation coefficient is proposed to obtain a stable and robust set of thermal key points. Combined with sensor reduction and classification, 15 sensors are reduced to 9 and classified into 3 groups. Finally, three sensors are selected as thermal key points. The sensor selection results are applied to a support vector machine model for a CNC grinding machine. The modeling results of 49 predictions based on 7 speed spectrums show that the root mean square error and maximum error are less than 2.32 μm and 3.73 μm, respectively. Compared with two traditional methods, the proposed method has higher accuracy and stronger robustness, which is effective for sensor selection of thermal error modeling.