Turnout System Research Articles

Surrogate model-based multi-objective optimization algorithm for train-ballast turnout system: Balancing safety and vibration damping in parameter design

In rail transportation structural parameter design, multi-objective optimal algorithms (MOOAs) have advanced significantly in safety, comfort, and aerodynamics domain. However, the vital factor of environment vibration remains neglected, impeding depot cover technology advancements. To address this challenge, with a focus on both safety and vibration damping. Firstly, the mechanistic models are constructed to establish a surrogate modeling database. Subsequently, a surrogate model is proposed, integrating stacked denoising autoencoders (SdAEs), self-attention (SA), and the gated recurrent unit (GRU). In addition, A novel MOOA, Dynamic Population-Multi-Objective Harris Hawks Optimization-Crowding Distance-Elitist Learning Strategy (D-MOHHO-CD-ELS), is proposed and its efficacy is validated against five existing MOOAs using the CTP1∼CTP7 test functions. Moreover, the train-ballast No.12 turnout system is considered as an engineering case. Importantly, the accuracy of both the mechanistic model and surrogate model is thoroughly validated. Among them, the performance of the data preprocessing and prediction algorithms is substantiated through a comparison of the proposed SdAEs-SA-GRU model against simulation results and eight other algorithms. Furthermore, the surrogate-based D-MOHHO-CD-ELS method, applied to tackle a multi-objective optimization problem with nine optimization parameters and four optimization objectives, demonstrates an efficiency improvement of 1 to 2 orders of magnitude compared to traditional methods. Consequently, following optimization, the wheel load reduction rate, derailment coefficient, capsizing coefficient and vibration damping effect are improved by 15.5%, 11.1%, 5.5% and 24.8% respectively, which reveals the effectiveness of enhancements in both the safety and damping efffect. the study offers a novel perspective for parameter design of TBT.

A method based on q-rung orthopair fuzzy cognitive map and TOPSIS method for failure mode and effect analysis considering risk causal relationships

As a preventive maintenance technique, failure mode and effect analysis (FMEA) is widely applied to determine and eradicate possible failures. In recent years, many FMEA methods have been developed to address the limitations of traditional FMEA methods in failure mode (FM) assessment, weight calculation, and FM prioritization in complex uncertain environments. However, due to the complexity of equipment, systems, services, etc., there is an inevitable influence on causal relationships between FMs, which is rarely considered by existing methods. Therefore, a novel FMEA method, which integrates the technique for order preference by similarity to the ideal solution (TOPSIS) of q-rung orthopair fuzzy set (q-ROFS) and q-rung orthopair fuzzy cognitive map (q-ROFCM), is proposed for reasoning the relationships between FMs. The design of the novel method, named q-ROFCM FMEA, involves three stages. The q-ROFS is applied to evaluate the risk factors for capturing more ambiguous information in the first stage. The q-ROFCM is proposed and the causal relationship between FMs is integrated to describe the impact of the relationships on the evaluation results through the dynamic behavior of FMs in the second stage. In the third stage, the TOPSIS method is applied to determine the priority ranking of FMs based on more reliable similarity measurement results of the projection measure. The proposed new method is applied to the FM risk assessment of power units in the railway turnout system. The competitiveness and effectiveness of the proposed method have been demonstrated by comparison with other classic methods and sensitivity analysis.

Fault Diagnosis of High-Speed Railway Turnout System Based on Improved Group Decision-Making

Turnout system is very important in the safety of railway system. However, the traditional maintenance method of turnouts is mainly based on human experience and basic intelligent model. Aiming at the problems existing in the current intelligent fault diagnosis method of turnout equipment, this paper uses the improved group decision-making method to dynamically and iteratively adjust and optimize the authority of each decision-making expert in the group decision-making method, so as to improve the prediction ability of the existing model, reduce the number of cases of prediction errors and further improve the safety of the turnout system.

Turnout Failure Diagnosis System Based on Group Decision Making Strategy

The turnout system emerges as the most critical component in railway infrastructure that possesses the function of controlling tracks where its faulty operations should be carefully concerned. The traditional turnout failure diagnosis is conducted manually, field staffs need to monitor thousands of turnout current curves based on expert experience per day, resulting in unstable diagnosis results. Thus, this paper utilized artificial intelligence and the group decision concept to propose a voting algorithm that considered the failure diagnosis results of three machine learning models, aiming to provide a feasible intelligent turnout failure diagnosis system with high accuracy. The training samples were filtered based on an experience-based method and fully cleansed for achieving more stable and reliable classification results. For evaluating the diagnosis performance, Recall and Precision were applied. As a result, the group decision failure diagnosis system indeed revealed high accuracy with low failure case omission.

Research on turnout system diagnosis based on group decision making

Safety has always been the primary consideration in high-speed railway operations. Turnouts are one of the most safety-critical components in the railway system, yet its fault diagnosis still relies on manual inspections, which is time-consuming and can lead to missing reports of turnout malfunctions. This paper proposes an automatic turnout fault diagnosis method based on group decision making. By assembling three individual classification algorithms, including the k-nearest neighbors algorithm, naive Bayes classifier, and deep neural network, this algorithm aims to automate turnout fault diagnosis and reduce the possibility of missing reports. Experiments to compare the performance of the group decision making algorithm and the three individual classifiers based on datasets generated by real turnout systems in simulated fault conditions are carried out. The result shows that the recall, i.e., the sensitivity to turnout fault of this algorithm is superior to the individual classifiers without losing overall accuracy, indicating that the missing report rate can be reduced through the group decision making process.

Application of Failure Mode Effects Analysis for Maintenance of Turnout System on Serviced Urban Rapid Transit

Over the past 25 years, maintenance processes for the wooden sleeper turnouts of sleeper floating tracks (known as STEDEF) have been insufficient, lacking accurate maintenance history and failure cause analyses. Currently, damaged parts are repeatedly replaced without an analysis of the cause of their failure. Therefore, it has been difficult to predict potential damages due to the lack of maintenance history data on the replaced items and the timing of the turnout component. This study proposes a turnout maintenance plan using the failure mode and effects analysis (FMEA) technique to analyze the turnout maintenance history data for a period spanning 10 years. The maintenance damage type of the turnout was classified to confirm the cause of damage and the damage frequency. Additionally, components that could lower the occurrence score were selected based on a risk analysis of the turnover. Therefore, it was possible to reduce the risk occurrence score by improving the size of the turnout top plate and the number of bolts.

Automated machine learning recognition to diagnose flood resilience of railway switches and crossings

The increase in demand for railway transportation results in a significant need for higher train axle load and faster speed. Weak and sensitive trackforms such as railway switches and crossings (or called ‘turnout’) can suffer from such an increase in either axle loads or speeds. Moreover, railway turnout supports can deteriorate from other incidences due to extreme weather such as floods which undermine cohesion between ballast leading to ballast washaway or loss of support under turnout structures. In this study, new intelligent automation based on machine learning pattern recognition has been built to detect and predict the deterioration of railway turnouts exposed to flooding conditions which is the scope of this study. Since the turnout system is very complex by nature, different features and smart filtering are explored to find the potential features for deep learning. Nonlinear finite element models validated by actual field measurements are used to mimic the dynamic behaviors of turnout supports under flooding conditions. The study exhibits that the novel recognition model can achieve more than 98% accuracy, yielding the potential capability to recognize and classify turnout support deteriorations facing extreme weather conditions which will be beneficial for responsible parties to schedule and plan maintenance activities.

A Fault-Diagnosis Method for Railway Turnout Systems Based on Improved Autoencoder and Data Augmentation

In recent years, with the rapid increase in coverage and lines, security maintenance has become one of the top concerns with regard to railway transportation in China. As the key transportation infrastructure, the railway turnout system (RTS) plays a vital role in transportation, which will cause incalculable losses when accidents occur. The traditional fault-diagnosis and maintenance methods of the RTS are no longer applicable to the growing amount of data, so intelligent fault diagnosis has become a research hotspot. However, the key challenge of RTS intelligent fault diagnosis is to effectively extract the deep features in the signal and accurately identify failure modes in the face of unbalanced datasets. To solve the above two problems, this paper focuses on unbalanced data and proposes a fault-diagnosis method based on an improved autoencoder and data augmentation, which realizes deep feature extraction and fault identification of unbalanced data. An improved autoencoder is proposed to smooth the noise and extract the deep features to overcome the noise fluctuation caused by the physical characteristics of the data. Then, synthetic minority oversampling technology (SMOTE) is utilized to effectively expand the fault types and solve the problem of unbalanced datasets. Furthermore, the health state is identified by the Softmax regression model that is trained with the balanced characteristics data, which improves the diagnosis precision and generalization ability. Finally, different experiments are conducted on a real dataset based on a railway station in China, and the average diagnostic accuracy reaches 99.13% superior to other methods, which indicates the effectiveness and feasibility of the proposed method.

Optimization of manipulator's motion mode on elastic base according to the criteria of the minimum central square value of drive torque

The results of studies of optimizing the mode of movement of the manipulator boom, mounted on an elastic base with a known stiffness the paper presents. The purpose of this scientific research is to reduce the oscillations of the manipulator turnout system, which will increase the overall efficiency of the manipulator, durability and reliability of the metal structure elements. The implementation of this goal have achieved by applying a controlled mode of operation of the drive with dynamic balancing of the drive mechanism. Using the Lagrange equation of the second kind, the equation of motion of the manipulator boom was compile and the expression for the generalized driving moment of the drive mechanism of the manipulator boom system was determined. This study considers only the angular displacement of the manipulator boom. The unbalanced drive driving moment of the manipulator boom had estimated by the component of the total inertial moment of the moving mass of the boom and load and the static load from the mass of the boom and load on the drive mechanism. The elastic base of the manipulator was present in the form of a linear spring with a given coefficient of elasticity. Since the main external factor of oscillation, perturbation in the metal structure of the manipulator is the driving moment of the drive, so we used the target optimization function, which estimates the root mean square value of the driving moment of the drive mechanism. The main criterion for optimizing the mode of motion was present in the form of an integral functional, and the search for its minimum value is carried out using the methods of calculus of variations.
 The results of this work can used by the drive control system at the design stage of the manipulator and during its operation. The dynamics of oscillations of such structural elements of boom systems of cranes is also estimated. The implementation of the obtained optimal modes of movement can be carried out using a hydraulic drive.

Effects of the moisture barrier and thermal liner components on the heat strain and thermal protective performance of firefighter turnout systems

An ideal firefighter turnout system should be capable of releasing body heat to prevent heat stress, indicated by THL (total heat loss) and Ref (evaporative resistance) indexes, while maintaining high thermal protective performance (TPP). Our study found no correlations between THL and Ref, or between Ref and TPP. The results showed that, when tested in the mild condition as in the standard THL test method, turnout systems with bi-component moisture barriers exhibited an advantage in THL that did not translate to more thermally stressful hot environments. A physiological manikin was used to understand the effect of turnout clothing systems on heat strain in different environmental conditions and the value of utilizing Ref or THL to predict heat strain performance. We found no difference in heat strain performance between composites with one-layer and two-layer spunlace thermal liners in mild or hot conditions. It showed that both THL and Ref had their limitations: THL only predicted thermal burden in mild environments, while Ref was only correlated in hot conditions. Thus, the exclusive reliance on either index could increase the risk of heat stress, and we recommend incorporating the Ref heat strain index, along with THL, as dual metrics for certifying the heat strain performance of turnout suits in the NFPA 1971 standard.

A hierarchical method based on improved deep forest and case-based reasoning for railway turnout fault diagnosis

Railway turnout system (RTS), which is widely laid along the railway tracks, is one of the most crucial devices in the whole railway infrastructures. Unpredictable outdoor weather, complex site conditions, and mechanical wear make the RTS the most vulnerable asset. Scientific and practical fault diagnosis is of great significance for reducing railway failures, improving operation efficiency, and ensuring transportation safety. In this paper, a hierarchical fault diagnosis method is proposed to realize accurate fault diagnosis of railway turnouts and improve the reliability and safety of railway systems. At the first level, an improved deep forest that embeds priori knowledge is adopted to distinguish the first-class fault sets. The embedded priori knowledge can help to outperform the traditional gcForest with fewer features produced at each level of the cascade forest. At the second level, a Case-based Reasoning component is utilized to classify the sub-class fault sets, which are difficult to be distinguished by the first level fault diagnosis due to their similar features. The hierarchical fault diagnosis method has been validated by using a real-world railway maintenance dataset. Extensive experiments show that the proposed method achieves superior performance compared to the state-of-the-art approaches in diagnostic precision under the constraint of limited data.

A Railway Turnout Closeness Monitoring Method Based on Switch Gap Images

The railway turnout system is a critical infrastructure that is responsible for steering trains. The closeness state of the turnout is directly related to the safety of the passing trains. To avoid derailment accidents caused by insufficient turnout closeness degrees, it is necessary to monitor the closeness degrees of the turnout. This article introduces a new condition-monitoring strategy that evaluates the turnout closeness degree by measuring the size of the switch gap. To implement this strategy, a detector based on image sensors is designed, and an automatic algorithm is proposed to measure the size of the switch gap based on the images acquired by the detector. The proposed algorithm directly processes the original images of the switch gap and can adaptively extract the region of interest (ROI) and the hysteresis threshold of the Canny operator for each image, which enables workers to avoid using a fixed ROI and hysteresis threshold with different images. Finally, the turnout closeness degree is calculated through a simple conversion of the size of the switch gap, measured by the proposed algorithm. Experiments based on data collected from the actual turnouts of a station show that the proposed turnout closeness monitoring method has high accuracy and robustness, which simplifies the turnout closeness maintenance process and improves its efficiency.

Sustainability and recyclability of composite materials for railway turnout systems

To minimize the maintenance frequency as well as the environmental impact caused by the growing number of railway materials reaching the end of their lives, appropriate sustainable management is inevitable prior to either turning them into wastes or leaving them in landfill sites. This paper presents the recent developments of Fibre-reinforced Foamed Urethane (FFU) sleepers installed in the railway turnouts. It determines the recycling and recovering processes of this infrastructure system. The results of recyclability (Rcyc) and recoverability (Rcov) rates for the railway turnout system are subject to assumptions associated with data which are taken from relevant sources (e.g. UNIFE). The result presents a value of 92.23% and 93.50%, respectively. A sensitivity analysis demonstrates that Mass Recovery Factor (MRF) has superior effect on Rcyc and Rcov values compared to Energy Recovery Factor (ERF). In addition, this study evaluates environmental life-cycle impacts based on the calculation of carbon emissions and energy consumption over 75 year’s lifespan. Comparison of the life-cycle sensitivity for the whole railway turnout system is conducted between FFU sleepers and concrete sleepers. The results show that concrete sleeper emitted almost the same carbon emission as the FFU material, but with much more maintenance and replacement frequency over the mentioned lifespan.

An Online Classification Method for Fault Diagnosis of Railway Turnouts.

Railway turnout system is a key infrastructure to railway safety and efficiency. However, it is prone to failure in the field. Therefore, many railway departments have adopted a monitoring system to monitor the operation status of turnouts. With monitoring data collected, many researchers have proposed different fault-diagnosis methods. However, many of the existing methods cannot realize real-time updating or deal with new fault types. This paper—based on imbalanced data—proposes a Bayes-based online turnout fault-diagnosis method, which realizes incremental learning and scalable fault recognition. First, the basic conceptions of the turnout system are introduced. Next, the feature extraction and processing of the imbalanced monitoring data are introduced. Then, an online diagnosis method based on Bayesian incremental learning and scalable fault recognition is proposed, followed by the experiment with filed data from Guangzhou Railway. The results show that the scalable fault-recognition method can reach an accuracy of 99.11%, and the training time of the Bayesian incremental learning model reduces 29.97% without decreasing the accuracy, which demonstrates the high accuracy, adaptability and efficiency of the proposed model, of great significance for labor-saving, timely maintenance and further, safety and efficiency of railway transportation.

The Effect of Unsupported Sleepers/Bearers on Dynamic Phenomena of a Railway Turnout System under Impact Loads

Track settlement is a common problem observed in ballasted railway tracks. The ballast bed and the material layers underneath it, deform under repeated trainloads and create uneven support conditions along the track. In some cases, the ballast settlement could be detrimental and the sleepers lose contact with the ballast bed partially or completely, resulting in higher contact forces and load distributions over the supported sleepers. Numerous studies have been conducted to investigate the phenomenon for normal tracks. Nevertheless, railway turnouts are somehow neglected. As a consequence, this study focuses on the relation between unsupported sleepers/bearers (particular name for turnouts) and a railway turnout system to develop the understanding of the response of turnout system under dynamic loadings. A 3D Finite Element Method (FEM) model is inherited from previous study and adopted to reflect the cases with unsupported bearer configurations. It is noteworthy that inherited model is capable of reflecting the impact forces, which is an inherent and fundamental characteristic of a railway turnout. Model verification is done with the parent model that was verified by field measurements. Three different support conditions (i.e., one, two, three unsupported bearers), five different velocities and six different positions of unsupported bearers are simulated. The results show that the performance of ‘fibre-reinforced foamed urethane’ (FFU) bearers are promising and more, unsupported bearers carry significant loads at particular locations, which is contrary to the sleepers on normal track that are subjected to insignificant loads.

Railway turnout system RUL prediction based on feature fusion and genetic programming

The remaining useful life (RUL) prediction of railway turnout systems (RTS) is very important to avoid unplanned shutdowns and reduce labor costs for the normal operation of railways. One key challenge on RUL prediction is how to construct an appropriate health indicator (HI) that can be utilized to infer conditions of RTS. Existing methods usually adopt some inherit merits (e.g., monotonicity, trendability, and robustness), and their prediction results lack real-world physical meaning due to their “black-box-like” property. In this paper, we present a novel feature fusion method for RUL prediction, which is able to capture the relationship between RUL and HI. A variant correlation-based feature selection method is utilized to extract features, which has the potential to depict the degradation process optimally, and then the selected features are fused by Auto-Associative Kernel Regression (AAKR) for prediction. To reduce the noise interference, the extracted features and the combined HI are all smoothed by using the locally weighted regression. Finally, a genetic programming (GP) algorithm is employed to predict the RUL of RTS. The proposed method is extensively tested on two turnout machine degradation datasets, and the results show that the proposed approach is effective for RUL prediction of RTS.

New Insights from Multibody Dynamic Analyses of a Turnout System under Impact Loads

A railway turnout is an essential infrastructure for managing railway traffic flexibility. In contrast, it imposes restrictions on train operations such as lower operational speeds through the turnout due to the complex movements of trains over the turnout. This results in the large-amplitude dynamic responses of the train-turnout interaction. Previous studies have focused on the train-turnout interactions entailing the wheel-rail contact forces and stresses. Very few of the studies considered the effects of the contact forces on the turnout structure and its components such as sleepers and bearers. Those previous studies neglected the dynamic forces and estimated the behavior of train-turnout interactions based on quasi-static calculations. In reality, turnouts are subjected to high impact forces, which can be higher than the permissible track forces. Consequently, a numerical model capable of determining impact forces was developed here, to evaluate the dynamic behaviors of a railway turnout and their effects on such turnout components as bearers, ballast, and so on. The model consists of a structured beam grillage laying on an elastic foundation with rigid wheelsets and a bogie. The model was verified by field measurements. The new insight stemmed from this study shows that neglecting the contribution of dynamic forces can result in the unsafe underestimation of train turnout behaviors.

Magnetic Field Test on an Electromagnetic Turnout Prototype System for High-T c Superconducting Maglev

Due to the strong magnetic force between segments of the permanent magnet guideway (PMG), the unmovable electromagnetic turnout becomes one of the most possible choices for high-temperature superconducting (HTS) maglev systems. Meanwhile, it has advantages of quicker response and smaller volume compared with mechanical turnout. A complete turnout system is vital for further evaluating feasibility. Based on the previous studies in ASCLab of Southwest Jiaotong University, a complete electromagnetic turnout prototype system is introduced in this article, which consists of two electromagnets, a Halbach type PMG, a control system, and a maglev vehicle model with a single-domain YBaCuO cylindrical bulk superconductor. In order to guarantee the stable running of vehicle at the turnout area, electromagnets are necessary to provide a similar magnetic field compared with the normal Halbach PMG. Based on the system, the measurement on the magnetic field was done and indicates that the electromagnet could replace permanent magnets of guideway to create a similar magnetic field distribution. The magnetic field experiment on this prototype system further certifies the applicability of the Halbach electromagnetic turnout. For future HTS maglev transportation, the electromagnetic turnout could be widely used in low-speed application such as railway station, switchyard, and laboratory.

Digital twin aided sustainability-based lifecycle management for railway turnout systems

Railway turnouts or so-called ‘switches and crossings’ are complex systems by nature of design and construction. Railway turnouts are used to change direction of trains from one to another. They require high-quality construction and maintenance, in order to minimise rapid degradation and component failures that could result in train derailments. Due to the complexity of railway turnouts, the efficiency and effectiveness of maintenance can be improved by integrating existing practice by Building Information Modelling (BIM). This research establishes and analyses the world's first 6D BIM for life cycle management of a railway turnout system. The BIM (Level 3) has integrated 6-dimensions of field data information based on Revit-2018 and Navisworks-2018 platforms. The digital twins of a railway turnout in 3D embrace time schedule, costs and sustainability across the whole life cycle. The use of BIM for railway turnout systems has the potential to improve the overall information flow of the turnout planning and design, manufacturing pre-assembly and logistic, construction and installation, operation and management and demolition, thereby achieving better project performance and quality. Based on integrated information of railway turnout system, the 6D BIM has the ability to assess on economic, management and sustainability, and achieve a balance among them. This is the word first to demonstrate that BIM can fully deliver its essential benefits by information sharing, easing technical communication, improving design quality, reducing of design errors, accelerating implementation, speeding up work, shortening construction duration, reducing construction costs, enhancing carbon efficiency, supporting project management, and providing its owners with higher operational efficiency over the railway turnout system life-cycle. The results reveal that embodied material emission is the main contributor towards carbon footprint, especially produced during the manufacturing stage. The reconstruction stage contributes the most expensive phase of life cycle. The insight will significantly benefit the co-value creation among engineers, project managers, technicians, and senior management team.

A Data-Driven Fault Diagnosis Method for Railway Turnouts

Turnout systems on railways are crucial for safety protection and improvements in efficiency. The statistics show that the most common faults in railway system are turnout system faults. Therefore, many railway systems have adopted the microcomputer monitoring system (MMS) to monitor their health and performance in real time. However, in practice, existing turnout fault diagnosis methods depend largely on human experience. In this paper, we propose a data-driven fault diagnosis method that monitors data from point machines collected using MMS. First, based on a derivative method, data features are extracted by segmenting the original sample. Then, we apply two methods for feature reduction: principal component analysis (PCA) and linear discriminant analysis (LDA). The results show that LDA gave a better performance in the cases studied. A problem that cannot be overlooked is that the imbalanced quantity of rare fault samples and abundant normal samples will reduce the accuracy of classic fault diagnosis models. To deal with this problem of imbalanced data, we propose a modified support vector machine (SVM) method. Finally, an experiment using real data collected from the Guangzhou Railway Line is presented, which demonstrates that our method is reliable and feasible in fault diagnosis. It can further assist engineers to perform timely repairs and maintenance work in the future.