System Identification I

Road and track irregularities have an important influence on the dynamic behaviour of vehicles. Knowledge of their characteristics and magnitude is essential for the design of the vehicle but also for comparable homologation and acceptance tests as well as for the planning and management of track maintenance. Irregularities of tracks and roads are regularly measured using various measurement technologies. All have advantages and weaknesses and require several processing steps. Characterisation of irregularities is done in the distance as well as in the wavelength domain. For rail irregularities, various distance domain description methods have been proposed and are in use. Methods have been analysed and compared with regard to their processing steps. Several methods have been analysed using measured irregularity and vehicle response data. Characterisation in the wavelength domain is done in a similar way for track and road irregularities. Here, an important issue is the estimation of the power spectral de...

Road and track irregularities: measurement, assessment and simulation

Track quality evaluation is fundamental for track maintenance. Around the world, track geometry standards are established to evaluate track quality. However, these standards may not be capable of detecting some abnormal track geometry conditions that can cause considerable vehicle-body vibration. And people gradually realized that track quality evaluation should be based not only on track geometry but also on vehicle performance. Vehicle-body vibration prediction is beneficial for locating potential track geometry defects, and the predicted accelerations can be used as an auxiliary index for assessing track quality. For this purpose, this paper gives a method to predict vehicle-body vibration based on deep learning, which represents one of the newest areas in artificial intelligence. By integrating convolutional neural network (CNN) and long short-term memory (LSTM), a CNN-LSTM model is proposed to make accurate and point-wise prediction. To achieve the optimal performance and explore the internal mechanism of the model, structural configurations and inner states are extensively studied. CNN-LSTM can take advantage of the powerful feature extraction capacity of CNN and LSTM, and outperforms the fully-connected neural network and the plain LSTM on the experimental data of a high-speed railway. In detail, CNN-LSTM has superior performance in predicting vertical vehicle-body vibration below 10 Hz and lateral vehicle-body vibration below 1 Hz. Moreover, analysis shows that the predicted vehicle-body acceleration can act as a performance-based evaluation index of track quality.

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Deep Learning for Track Quality Evaluation of High-Speed Railway Based on Vehicle-Body Vibration Prediction

Track geometry quality and dynamic vehicle response are closely related, but do not always correspond with each other in terms of maximum values and standard deviations. This can often be seen to g ...

Correlation of track irregularities and vehicle responses based on measured data

A Kalman filter algorithm for identifying track irregularities of railway bridges using vehicle dynamic responses

Testing of the running characteristics of railway vehicles requires well-defined boundary conditions like track geometry quality. Test sections with the same track geometry quality must lead to the same dynamic vehicle response (VR) forces. The state-of-the-art methods do not fulfil this important requirement for a track quality definition. Our proposed method for track geometry assessment considers the vehicle/track interaction. 'Representative' transfer functions are used for the prediction of the vehicle reaction. Therefore, the results show a significant enhancement of the correlation between the track assessment quantities and the VR forces.

Bernd Luber

Papers

Road and track irregularities: measurement, assessment and simulation

Track geometry evaluation method based on vehicle response prediction

Methods for Classification and Track Geometry Evaluation Based on Vehicle Response Analysis