Zhi Han’s research while affiliated with China Academy of Railway Sciences and other places

The kinematic posture of the bogie within comprehensive inspection trains exerts a profound influence on the precision of the measurement systems. This study develops an advanced posture calculation algorithm by integrating data from multiple accelerometers. Rigorous analytical evaluations of the bogie’s central motion posture within the comprehensive inspection train delineate a pronounced escalation in the magnitude of this motion posture concomitant with increased velocities, thereby unveiling a robust correlation in posture outcomes at consistent speeds. Confronting the limitations inherent in the multi-accelerometer amalgamation approach, particularly its diminished sensitivity to posture angle variations precipitated by track geometry anomalies, this research introduces an avant-garde posture compensation paradigm. This paradigm has been corroborated through meticulously constructed experimental models. The rectified outcomes for lateral and vertical displacements, and for roll and nodding angles in posture computation manifest peak errors of 0.09, 0.01, 0.02, and 0.03, respectively, alongside absolute errors amounting to 0.17, 0.04, 0.08, and 0.06, respectively. These empirical findings provide robust substantiation for the analytical dissection of measurement systems accuracy aboard comprehensive inspection trains under diverse operational scenarios. Such revelations bear significant connotations for augmenting the precision and reliability of contemporary railway inspection methodologies.

The detection and recognition of track geometry are crucial for ensuring railway transportation safety and maintenance efficiency. Traditional linetype recognition algorithms struggle to effectively handle the complexities of variable track geometry and noise interference. To address these challenges, a novel linetype recognition algorithm (NLRA) is proposed, which thoroughly analyzes the characteristics of track curvature across straight lines, transition curves, and circular segments. By employing partial differential equations for data preprocessing, NLRA effectively filters out noise. Utilizing curve design parameters and integrating line anti-noise recognition with multi-segment line fitting techniques, the algorithm accurately identifies each line segment and incorporates a single-point recognition method for data gaps. Experimental results demonstrate that NLRA outperforms traditional algorithms in terms of real-time performance, recognition accuracy, anti-interference capability, and noise handling. Notably, NLRA enhances accuracy by at least 35.1% and recall by at least 17.4% on selected typical routes. This work underscores the algorithm’s potential to provide efficient and accurate tools for track maintenance, facilitate advancements in track detection software, and significantly contribute to railway transportation safety.

With the rapid development of railway construction, the mileage of railway detection has increased dramatically, and railway companies have higher requirements for the repeatability and accuracy of track geometric dynamic detection data. Therefore, the track geometry detection system needs to be calibrated to improve the measurement accuracy to ensure the safety of railway operation. However, the existing calibration methods of the track geometry detection system can only realize the static calibration and lack the dynamic calibration method, which cannot calibrate the track geometry detection system under the real operation state. Therefore, a dynamic calibration method for the track geometry detection system is proposed in this paper. A real and reliable test environment is provided for the track geometry detection system by presetting the track geometry irregularity and laying the rail dynamic displacement monitoring system on the calibration test line, and the dynamic and static differences of the track are analyzed based on the test environment. Based on the bisection method, this paper reduces the measurement error of the track geometry detection system and analyzes the measurement repeatability of the track geometry detection system. Finally, the error correction model of the track geometry detection system is established, and the error analysis and uncertainty evaluation of the track geometry detection system are carried out. The results show that the track geometry detection system calibrated by the dynamic calibration method proposed in this paper meets the requirements of the measurement error for the gauge and cross level. This study can establish the value traceability relationship of the track geometry detection system, realize the calibration and evaluation of the accuracy of the track geometry detection system under the real operation state, and improve the reliability and accuracy of the track geometry dynamic detection results.