In the point cloud coordinate conversion algorithm, the trajectory and attitude are interpolated, and the coordinates are converted according to the joint calibration parameters. In terms of 3D point cloObjective With the expansion of railway operation scale and the increase in service time, the demand for the efficiency of track condition inspection and maintenance has risen remarkably. The traditional manual track inspection or hand-pushed equipment can hardly meet the requirements of modern railways for inspection efficiency and accuracy. Non-contact methods based on on-board dynamic inspection, such as the combination of optical and inertial sensors, can efficiently complete the measurement of track geometric parameters, profiles, and other key indicators. However, the measurement of single sensors (for example, line-structured optical sensors) under dynamic conditions has problems like trajectory distortion, especially the limited accuracy in the direction of rail extension, which becomes a key factor restricting the accuracy of track 3D point cloud construction. Compensating the platform vibration in a high dynamic environment with the position and attitude changes measured by inertia and constructing a real 3D point cloud can enhance the dimension of detection information and have a wide application prospect. Currently, there are no related mature methods and applications for high-precision line structured light 3D point cloud construction. In our study, an inertial trajectory measurement method with the fusion of odometry and IMU is proposed, which has a high short-term accuracy retention ability and improves the accuracy of the 3D point cloud. The point cloud data is applied to the field of track geometric parameter measurement. Based on the point cloud data, the vertical and horizontal 10 m chord measurements, and the level are calculated to realize the application value of the data. Methods To address the above issues, we propose a real-time construction method of track 3D point clouds based on multi-sensor fusion. The multi-sensor fusion point cloud calculation method is shown in Fig. 3. Firstly, we improve the short-term holding accuracy of inertial navigation by optimizing the initial alignment algorithm of the inertial measurement system and the inertial attitude solving algorithm within the framework of extended Kalman filtering. Then, we design the coordinate conversion method of point cloud construction, which converts the point cloud data measured by the line-structured optical sensors to the geographic coordinate system. Finally, the geometric parameters of the track are projected according to the 3D point cloud to achieve the application of the point cloud data. The train cannot start and stop randomly in the actual detection task, so it is necessary to achieve the dynamic and rapid alignment of the attitude. We construct and solve the Wahba equation and optimize the alignment algorithm based on the inertial system. In terms of inertial trajectory solution, the measurement equation is derived based on the specific force equation, which directly observes the misalignment angle and the zero deviation of the plus meter. The measurement equation is derived based on the trajectory recursive equation, which directly observes the misalignment angle, the position error, and the scale coefficient of the odometer. Based on the extended Kalman filter framework, the fusion filtering of the odometer and IMU data is realized. ud data application, a typical application scheme of the track point cloud is introduced, which is the non-contact rapid measurement of track geometric parameters. Results and Discussions The feasibility and accuracy advantages of the method are verified through the special train experiments of the Beijing Circular Railway Test. 1) The heading angle error varies greatly in the initial 500 s, and then gradually stabilizes within 1 degrees . The horizontal attitude angle error is rapidly reduced to below 0.1 degrees within 20 s. The alignment angle error is close to the theoretical one, and the alignment accuracy is close to the theoretical value and can be applied to the actual working conditions of train detection. 2) As shown in Fig. 7, based on the method proposed in our study, the measurement error of the roll angle is within -0.025 degrees - 0.010 degrees , and the measurement error of pitch angle is within +/- 0.01 degrees, which shows high accuracy. As shown in Fig. 8, after accurate calibration, the mounting declination in the pitch direction is -0.05 degrees . After considering the installation deviation angle, the accuracy of elevation measurement is obviously improved, and the elevation drift is only 3.92 m when the train travels for 50 km. The elevation error increases the fastest in the section from 439860 to 439944 sin the figure, the cumulative mileage is about 1.6 km, and the elevation error has increased by 1.3 m, which shows that the elevation error is within 0.81% of the cumulative mileage. As shown in Fig. 9, the train travels about 91.7 km cumulatively, the maximum deviation of plane is 290.84 m, and the plane deviation is about 3.17% of the mileage. Based on the comprehensive above data, the accuracy maintenance ability of the inertial measurement method proposed in our study can be illustrated. 3) The measurement repeatability of the vertical 10 m chord measurements is used as an evaluation index, and the repeatability of the method in our study is 0.71 mm. The measured and reference values for vertical and horizontal 10 m chord and level are compared in Fig. 12, and the trend of the waveforms of the two detections is more consistent. The peak values of the measured values and reference values at 11 preset working conditions are shown in Table 1. The maximum error value of the horizontal 10 m chord and level is 0.41 mm, and the maximum error value of the vertical 10 m chord is 1.07 mm. The rail direction and horizontal value better represent the measurement accuracy of the system, and the measurement deviation in the actual unevenness parameter accounts for a maximum of 4.8%, which can satisfy the accuracy requirement of practical application. Conclusions The method proposed in our study does not require static initial alignment and can dynamically complete the attitude initialization and detection tasks with the start and stop of the train, which solves the influence of train scheduling constraints on the detection accuracy. The multi-sensor fusion significantly improves the short-time accuracy retention ability and ensures the stability of local accuracy by reducing the correction frequency. This method has a wide application prospect in the field of railway condition monitoring, which can provide strong technical support for track disease identification and real-time maintenance and provide a guarantee for improving the safety and efficiency of railway operation.
This paper addresses the challenges associated with quickly computing far-field radiated noise originating from the bogie zone of high-speed trains.Initially,an assumption was given that countless dipole sound sources are distributed on the train surface,re-flecting the predominant nature of dipole sound sources in the aerodynamic noise of high-speed trains.This assumption led to the estab-lishment of an identification method,which was then used to identify sound sources in the bogie zone,with the support of Computational Fluid Dynamics(CFD).Through this identification process,the main dipole aerodynamic sources were found to be located near the walls of 11 components within the bogie zone,including cow catchers and front wheels.Subsequently,computational methods were estab-lished to determine the sound source intensity and far-field noise respectively for these components.The simulation results at three differ-ent speeds were utilized to establish a relational expression of the far-field sound pressure level of each component concerning frequen-cies,train speeds,characteristic sizes,and propagation distances through fitting.The far-field radiated noise from the 11 components was aggregated to create an empirical formula for calculating far-field noise from the bogie zone.These findings demonstrate the satisfactory accuracy of the fitted formula in quickly computing the far-field aerodynamic noise of the bogie zone,based on models of varying scales for high-speed trains.This methodology also offers a reference for establishing computational methods for aerodynamic noise radiation of other types of vehicles.
Objective Accurate train location information is crucial for communications-based train control(CBTC)systems to ensure safe operation.With the current trend towards vehicle-to-vehicle communication and fully automatic operation(FAO)for subways,CBTC systems demand highly ac-curate and real-time train positioning.Existing research into train positioning using visible light communication(VLC)achieves train location in-formation by calculating the positional relationship between the receiving and sending ends.This attracts attention when a train is stationary or running at a uniform speed.However,little consideration has been paid to the accumulated error from the inertial measurement unit(IMU)and the zero-velocity offset of the IMU.The zero velocity update(ZUPT)algorithm can correct IMU errors by detecting the vehicle's motion state and at-titude angle to improve the accuracy and stability of positioning.The algorithm is simple,easy to implement,and has many applications in vehicle inertial navigation,robot positioning,human motion recognition,and other fields.This study proposes a continuous train positioning method based on VLC and ZUPT to optimize the results of dynamic train positioning and correct the offset from the IMU when a train is pulled over at a station,enabling seamless train positioning with high accuracy along an entire subway line.The proposed method can improve the accuracy of train positioning in tunnel environments and provide a reference for autonomous train positioning of CBTC systems toward vehicle-to-vehicle communication. Methods This study uses the LED light sources on the tunnel wall as the sending ends of VLC,while the binocular complementary metal-oxide-semiconductor(CMOS)camera fixed on the top carrier platform of the train head serves as the receiving end.This setup enables seamless train positioning with high accuracy along the entire subway line.First,based on the principle of VLC,the binocular CMOS camera at the head of the train captures images of the LED lamps,and the train simultaneously receives the corresponding strip information of the LED lamps using the blinking frequency information.Then,combined with IMU data,mechanical vibration at the receiving end is compensated to obtain the results of dynamic train positioning.In addition,to address the errors caused by train movement and when it is stationary,the train operation process over the entire line is divided into moving and zero-velocity sections.A zero-velocity detection condition is adopted to determine whether the train is stationary,and the results of dynamic train positioning are further optimized using the Unscented Kalman Filter(UKF)algorithm.Finally,the zero-velocity offset from the IMU is corrected to compensate for the divergence in the position and attitude estimation accuracy of the receiving end on the top of the train,achieving continuous high-precision positioning in the tunnel environment.The actual data from train operations on Chengdu Metro Line 1 are utilized to verify the feasibility of the proposed method.An experimental platform for autonomous train positioning,combining binocular stereo vision and VLC,is established,and MATLAB software is employed to analyze the experimental results of continuous train posi-tioning. Results and Discussions A test point of train positioning is set every 0.5 m along the direction of train movement,and experiments are conducted 20 times on the optimization of train positioning in each positioning unit.When the train is running at speeds of 40,60,and 80 km/h,the maxim-um error of train positioning after optimization with the unscented Kalman filter(UKF)is 15.27,16.93,and 19.57 cm,respectively,and the accur-acy of train positioning improves by 53.06％,48.46％,and 52.71％,respectively(Fig.5).After optimization with the UKF,the maximum error of train positioning during motion with constant acceleration is 18.48 cm,and during motion with constant deceleration,it is 18.24 cm(Fig.6).The error of train positioning does not entirely change with the increase or decrease in train speed and still maintained a shrinking trend as the train ap-proached the LED lamps(Fig.7).When the train enters station B during motion with constant deceleration and left station B during motion with constant acceleration,the average error in a unit of train positioning using VLC is reduced to 6.52 cm(Fig.8),and the error of train positioning at the stopping point is decreased to 15.18 cm(Fig.9)after optimization with ZUPT-UKF.The proposed method meets the requirements of the IEEE 1474.1-2004 standard for train positioning and ensures continuous high-precision positioning throughout the entire line. Conclusion This study provides a train positioning method adopting VLC and the ZUPT algorithm to optimize the results of train positioning in the moving section and address the IMU's accumulated error during stopping time in a tunnel environment.The experimental results showed that the maximum error of train positioning is 15.27,16.93,and 19.57 cm,respectively,when the train runs in a straight line at constant speeds of 40,60,and 80 km/h after UKF optimization.When the maximum target speed is 80 km/h,the maximum errors corresponding to constant accelera-tion and deceleration are 18.48 and 18.24 cm,respectively.After UKF optimization in the zero-speed section,the average train positioning error decreases from 18.74 to 6.52 cm,and the IMU's accumulated error significantly declines.After ZUPT-UKF optimization,the average train posi-tioning error reduces from 11.35 to 6.05 cm,and the train positioning error at the stop point diminishes from 30.55 to 15.18 cm,effectively sup-pressing the IMU's zero-speed displacement.The proposed method meets the requirements of CBTC systems oriented toward vehicle-to-vehicle communication for the high accuracy of train positioning and ensures continuous high-precision train positioning throughout the entire line.
During shield tunneling in soft soil areas,mud or muddy clay layers are often encountered.Before the excavation of the connecting passage in the shield tunnel section,it is necessary to reinforce the soil to improve its strength and stability,and ensure construction safety.This article takes the first municipal railway project in Taizhou as an example,using freezing method construction technology to reinforce the soft soil of the connecting passage,studying the freezing method construction technology,and conducting statistical analysis of the freezing effect.It is concluded that the frozen soft soil can meet the excavation strength requirements,which indicates that using freezing method construction can meet the construction requirements and safety of the connecting passage in Taizhou's soft soil,and can provide technical support for the next excavation construction step.It also provides reference for engineering construction in similar soft soil areas.
