Identification of track irregularities with the multi-sensor acceleration measurements of vehicle dynamic responses

被引:8
作者
Guo, Xiangying [1 ]
Li, Changkun [1 ]
Luo, Zhong [2 ]
Cao, Dongxing [1 ]
机构
[1] Beijing Univ Technol, Fac Mat & Mfg, Beijing Key Lab Nonlinear Vibrat & Strength Mech S, Beijing, Peoples R China
[2] Northeastern Univ, Sch Mech Engn & Automat, Shenyang, Peoples R China
基金
中国国家自然科学基金;
关键词
Track irregularity identification; condition monitoring; dynamic responses; onboard measurements; attitude calculation; unknown input observer; RAILWAY; MODEL;
D O I
10.1080/00423114.2023.2200193
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Track irregularities induce potential risks to the safety and stability of railway track systems. This paper proposes a novel methodology to identify vertical and lateral track irregularities. The method involves measuring system-based attitude calculation and a model-based unknown input observer estimator, based on the dynamic responses of distributed multi-sensors on the vehicle and bogie. First, a mechanical model of wheel-rail contacts is built with dynamic methods. The model considers the different directions of motion for a railway vehicle and consists of two bogies and four wheelsets. Based on the multi-sensor acceleration measurement, the vertical and lateral acceleration signals of the vehicle and bogies are integrated into the displacement signal. Then a state-space description of the vehicle suspension model is established for inverse dynamical analysis to extract the input signals. A suitable unknown input observer is constructed to estimate the track irregularities by transforming the state space equations of the vehicle into an augmented system that can monitor the track irregularities in-service. This method provides an opportunity to reduce the costs of the monitoring infrastructure and provide quicker and more reliable information about the status of a track.
引用
收藏
页码:906 / 931
页数:26
相关论文
共 38 条
[1]   An alternative procedure to measure railroad track irregularities. Application to a scaled track [J].
Aceituno, Javier F. ;
Chamorro, Rosario ;
Munoz, Sergio ;
Escalona, Jose L. .
MEASUREMENT, 2019, 137 :417-427
[2]   Railway track geometry determination using adaptive Kalman filtering model [J].
Akpinar, Burak ;
Gulal, Engin .
MEASUREMENT, 2013, 46 (01) :639-645
[3]  
Briales E., 2021, MEAS MAHWAH N J, V184
[4]  
Chang C., 2021, MEAS MAHWAH N J, V170
[5]  
Chellaswamy C., 2020, MEAS MAHWAH N J, V152
[6]   Railroad Track Condition Monitoring Using Inertial Sensors and Digital Signal Processing: A Review [J].
Chia, Leonard ;
Bhardwaj, Bhavana ;
Lu, Pan ;
Bridgelall, Raj .
IEEE SENSORS JOURNAL, 2019, 19 (01) :25-33
[7]   Estimation of lateral and cross alignment in a railway track based on vehicle dynamics measurements [J].
De Rosa, Anna ;
Alfi, Stefano ;
Bruni, Stefano .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2019, 116 :606-623
[8]   Identification of structural stiffness and excitation forces in time domain using noncontact vision-based displacement measurement [J].
Feng, Dongming ;
Feng, Maria Q. .
JOURNAL OF SOUND AND VIBRATION, 2017, 406 :15-28
[9]  
Guo X., 2021, APPL SCI-BASEL, V11
[10]   Monitoring and tracking of a suspension railway based on data-driven methods applied to inertial measurements [J].
Hesser, Daniel Frank ;
Altun, Kubilay ;
Markert, Bernd .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2022, 164