Compensation of axle-generator errors due to wheel slip and slide

Abstract

Significant errors of train axle generators (tachometers) are due to wheel slip and slide. In this paper, an algorithm is designed to compensate for these errors. The algorithm identifies the wheel slip and slide by examining the variation of the processed vehicle longitudinal acceleration. Whenever wheel slip/slide is identified, then the vehicle speed is adjusted if a certain condition is met. The adjustment is a simple linear interpolation between the two speed values recorded before and after wheel slip/slide detection. In addition, a speed and acceleration observer using a Kalman filter is implemented. Experimental results using three different axle encoders aboard a freight train are provided to illustrate the performance of the proposed algorithm.

Position computation models for high-speed train based on support vector machine approach

“…(14), we know that there are only two parameters C and P in LSSVM model need to be set before training. Similar to, model 2, the process of parameter updating for model 3 is shown as below: the new training data set will be added to train model 3 to get the updating parameters in Eq.…”

Slip and Slide Detection and Adaptive Information Sharing Algorithms for High-Speed Train Navigation Systems

“…It is necessary to check if the velocity estimate from the tachometer is within the normal range in order to detect the slip and slide errors. In general, the slip and slide errors tend to occur when the train is accelerating, decelerating or moving along a curved track [11]. In HST, it has been found that the slip and slide errors can cause a position error of from tens of meters to hundreds of meters.…”

“…As a good reference, velocity estimation from a GPS receiver is used to compare with tachometer readings and to detect the wheel slip and slide [6]. And in [11], the authors use a Kalman filter (KF) to estimate the velocity and acceleration of the train and propose a slip and slide detection (SSD) technique to compare tachometer readings with the estimated states. In a viewpoint, the previous studies on the SSD are based on a mere comparison to a reference value measured by a single sensor such as GPS or on the mutual comparison of tachometer readings.…”

“…With the FKF based on the feedback scheme and information sharing in [11], the initial error covariance bounds are uncorrelated. The estimates of local filters in the FKF with a feedback scheme are mutually independent and there exists no correlation when the initial states of local filters are uncorrelated with each other [12].…”

“…The estimates of local filters in the FKF with a feedback scheme are mutually independent and there exists no correlation when the initial states of local filters are uncorrelated with each other [12]. The estimate and its error covariance of the master filter are passed to local filters with the following rules in [11].…”