In order to accurately and quickly measure the spatial straightness of train axle, a measurement system of train axle spatial straightness was constructed and the algorithms of spatial circle fitting, spatial straight line fitting and straightness measurement were studied. Firstly, the spatial circle fitting algorithm based on spatial plane and spatial sphere tangent was introduced according to the characteristics of the object under test. Then, the RANdom SAmple Consensus (RANSAC) algorithm was used to iterate out the best point set of the model. On the basis of the data obtained from the spatial circle fitting of the train axle section, the data of the train axle section spatial circle center was analyzed. And the wolf colony algorithm was used to fit the spatial straight line, that is, according to the circle center coordinates of the spatial circle of the train axle section at the position of the space section, the spatial straight line of the train axle was fitted. Finally, the wolf colony algorithm was used to measure the spatial straightness of train axle, and the measured data were compared with the data of laser tracker. Experimental results show that the accuracy of measuring the spatial straightness of train axle based on wolf colony algorithm is 0.01 mm, which can meet the requirements of high accuracy, high stability and repeatability in measurement of the spatial straightness of train axle.

It is difficult to realize on-line measurement for the large dimension range of China Railway High-speed (CRH) body, the complexity of testing items and the variety of vehicles. Firstly, a measurement scheme of key dimensions for a large-scale bullet train was proposed, where binocular Charge Coupled Device (CCD) stereo vision was used to set up the measuring sub stations of each key dimension, and the laser tracker and coordinate transformation algorithm were used to complete the global calibration of each CCD camera's measuring sub station. In each measuring sub station, the stereo spatial ball detection technology was used to measure local key dimensions. At the same time, a neural network temperature error compensation model based on wavelet analysis was constructed, and the precision of space distance compensation reached 0.05 mm. The comparison between the proposed method and three-coordinate measuring machine, shows that the proposed method is simple in operation, high in flexibility and high in precision, which can effectively solve the key dimension detection problem of CRH body.