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Register a new userIdentification of Behavioural Models for Railway Turnouts Monitoring
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This study introduces a low-complexity behavioural model to describe the dynamic response of railway turnouts due to the ballast and railpad components. The behavioural model should serve as the basis for the future development of a supervisory system for the continuous monitoring of turnouts. A fourth order linear model is proposed based on spectral analysis of measured rail vertical accelerations gathered during a receptance test and it is then identified at several sections of the turnout applying the Eigensystem Realization Algorithm. The predictviness and robustness of the behavioural models have been assessed on a large data set of train passages differing for train type, speed and loading condition. Last, the need for a novel modeling method is argued in relation to high-fidelity mechanistic models widely used in the railway engineering community.
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The railpad is a key element in railway infrastructures that plays an essential role in the train-track dynamics. Presence of worn or defective railpads along railway track may lead to large wheel/rail interaction forces, and a high rate of deterioration for track components. Despite the importance of railpad, the track infrastructure managers use no inspection tool for monitoring in-service railpads over time. In this paper, a novel data-driven monitoring tool for long-term performance analysis of in-service railpads is developed based on train-induced vibration data collected by a track-side measurement system. The monitoring tool consists of a method for track resonance frequencies estimation, a temperature-frequency model for describing railpad behavior with respect to ambient temperature, and a generalized likelihood ratio test based on the generalized extreme value distribution for detecting changes in the railpad status over time. To evaluate the performance of the proposed monitoring system, the status of railpads at four different locations along a railway turnout is monitored over a period of 18 months. It is shown that the monitoring system can successfully detect changes in railpad properties over the considered period.
We propose two optimization-based heuristics for structure selection and identification of PieceWise Affine (PWA) models with exogenous inputs. The first method determines the number of affine sub-models assuming known model order of the sub-models, while the second approach estimates the model order for a given number of affine sub-models. Both approaches rely on the use of regularization-based shrinking strategies, that are exploited within a coordinate-descent algorithm. This allows us to estimate the structure of the PWA models along with its model parameters. Starting from an over-parameterized model, the key idea is to alternate between an identification step and structure refinement, based on the sparse estimates of the model parameters. The performance of the presented strategies is assessed over two benchmark examples.
In order to ensure the personal safety of the drivers and passengers of rail transit in plateau environment, the vital signs and train conditions of the drivers and passengers are taken as the research object, and the dynamic relationship between them is studied and analyzed. In this paper, subway drivers under normal operation conditions are taken as research objects to establish the vital signs monitoring and early warning system. The vital signs data of the subway drivers, such as heart rate (HR), respiratory rate (RR), body temperature (T) and blood oxygen saturation (SPO2) of the subway driver are collected by the head-mounted sensor, and the least mean square adaptive filtering algorithm is used to preprocess the data and eliminate the interference information. Based on the improved BP (Back Propagation) neural network algorithm, a prediction model is established to predict the vital signs of subway drivers in real-time. We use the early warning score evaluation method to measure the risk of subway drivers vital signs, and then the necessary judgment basis can be provided to dispatchers in the control center. Experiments show that the system developed in this paper can accurately predict the evolution of subway drivers vital signs, and timely warn the abnormal states. The predicted value of vital signs is consistent with the actual value, and the absolute error of prediction is less than 0.5 which is within the allowable range.
We study the routing policy choice problems in a stochastic time-dependent (STD) network. A routing policy is defined as a decision rule applied at the end of each link that maps the realized traffic condition to the decision on the link to take next. Two types of routing policy choice models are formulated with perfect online information (POI): recursive logit model and non-recursive logit model. In the non-recursive model, a choice set of routing policies between an origin-destination (OD) pair is generated, and a probabilistic choice is modeled at the origin, while the choice of the next link at each link is a deterministic execution of the chosen routing policy. In the recursive model, the probabilistic choice of the next link is modeled at each link, following the framework of dynamic discrete choice models. The two models are further compared in terms of computational efficiency in estimation and prediction, and flexibility in systematic utility specification and modeling correlation.
Identification of abnormal source hidden in distributed parameter systems (DPSs) belongs to the category of inverse source problems. It is important in industrial applications but seldom studied. In this paper, we make the first attempt to investigate the abnormal spatio-temporal (S-T) source identification for a class of DPSs. An inverse S-T model for abnormal source identification is developed for the first time. It consists of an adaptive state observer for source identification and an adaptive source estimation algorithm. One major advantage of the proposed inverse S-T model is that only the system output is utilized, without any state measurement. Theoretic analysis is conducted to guarantee the convergence of the estimation error. Finally, the performance of the proposed method is evaluated on a heat transfer rod with an abnormal S-T source.
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