Continuous prediction method of earthquake early warning magnitude for high-speed railway based on support vector machine

Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wave arrival,the prediction time window was established at an interval of 0.5 s.12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning(EEW)magnitude prediction model(SVM-HRM)for high-speed railway based on SVM.Findings–The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm.Results show that at the 3.0 s time window,themagnitude prediction error of the SVM-HRMmodel is obviously smaller than that of the traditionalτc method and Pd method.The overestimation of small earthquakes is obviously improved,and the construction of the model is not affected by epicenter distance,so it has generalization performance.For earthquake events with themagnitude range of 3–5,the single station realization rate of the SVM-HRMmodel reaches 95%at 0.5 s after the arrival of P-wave,which is better than the first alarm realization rate norm required by“The TestMethod of EEW andMonitoring Systemfor High-Speed Railway.”For earthquake eventswithmagnitudes ranging from3 to 5,5 to 7 and 7 to 8,the single station realization rate of the SVM-HRM model is at 0.5 s,1.5 s and 0.5 s after the P-wave arrival,respectively,which is better than the realization rate norm of multiple stations.Originality/value–At the latest,1.5 s after the P-wave arrival,the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate,which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.

Heterogeneity identification method for surrounding rock of large-section rock tunnel faces based on support vector machine

Purpose–The purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock tunnels of high-speed railways.Design/methodology/approach–Relying on the support vector machine(SVM)-based classification model,the nominal classification of blastholes and nominal zoning and classification terms were used to demonstrate the heterogeneity identification method for the surrounding rock of tunnel face,and the identification calculation was carried out for the five test tunnels.Then,the suggestions for local optimization of the support structures of large-section rock tunnels were put forward.Findings–The results show that compared with the two classification models based on neural networks,the SVM-based classification model has a higher classification accuracy when the sample size is small,and the average accuracy can reach 87.9%.After the samples are replaced,the SVM-based classification model can still reach the same accuracy,whose generalization ability is stronger.Originality/value–By applying the identification method described in this paper,the significant heterogeneity characteristics of the surrounding rock in the process of two times of blasting were identified,and the identification results are basically consistent with the actual situation of the tunnel face at the end of blasting,and can provide a basis for local optimization of support parameters.

Data-driven technology of fault diagnosis in railway point machines:review and challenges

Safety and reliability are absolutely vital for sophisticated Railway Point Machines(RPMs).Hence,various kinds of sensors and transducers are deployed on RPMs as much as possible to monitor their behaviour for detection of incipient faults and anticipation using data-driven technology.This paper firstly analyses and summarizes six RPMs’characteristics and then reviews the data-driven algorithms applied to fault diagnosis in RPMs during the past decade.It provides not only the process and evaluation metrics but also the pros and cons of these different methods.Ultimately,regarding the characteristics of RPMs and the existing studies,eight challenging problems and promising research directions are pointed out.

Research on fault diagnosis of a railway point machine based on a multi-entropy feature extraction method and support vector machine

A new fault diagnosis method is proposed to effectively extract the fault features of the sound signal of typical faults of ZDJ9 railway point machines.A multi-entropy feature extraction method is proposed by combing multi-scale permutation entropy and wavelet packet entropy.Firstly,empirical mode decomposition is performed on sound signals to obtain modal components with different time scales.Then,multi-scale permutation entropy is extracted from these components.Meanwhile,the wavelet packet entropy of the sound signals of these sensitive nodes is obtained by analysing the reconstructed signals of the last layer nodes.Since the multi-scale permutation entropy and the wavelet packet entropy can distinguish the subtle features of the signal,the subtle features of the information among the high-dimensional features,ReliefF is utilized.Finally,a support vector machine(SVM)is used to judge the original sismal can be obtained as the feature vector of the 2DJ9 iway point mnchine in ditterent states,To reduce the redundant fault type of a ZDJ9 rilway point machine.

Fault diagnosis of railway point machines based on wavelet transform and artificial immune algorithm

Aiming at the current problems of high failure rate and low diagnostic efficiency of railway point machines(RPMs)in the railway industry,a short-time method of fault diagnosis is proposed.Considering the effect of noise on power signals in the data acquisition process of the railway centralized signaling monitoring(CSM)system,this study utilizes wavelet threshold denoising to eliminate interference.The results show that the accuracy of fault diagnosis can be improved by 4.4% after denoising the power signals.Then in order to attain a lighter weight and shorten the running time of the diagnosis model,Mallat wavelet decomposition and artificial immune algorithm are applied to RPM fault diagnosis.Finally,voluminous experiments using veritable power signals collected from CSM are introduced,which show that combining these methods can procure higher precision of RPMs and curtail fault diagnosis time.This substantiates the validity and feasibility of the presented approach.

Condition monitoring and fault diagnosis strategy of railway point machines using vibration signals

Condition monitoring of railway point machines is important for train operation safety and effectiveness.Referring to the fields of mechanical equipment fault detection,this paper proposes a fault detection and identification strategy of railway point machines via vibration signals.A comprehensive feature distilling approach by combining variational mode decomposition(VMD)energy entropy and time-and frequency-domain statistical features is presented,which is more effective than single type of feature.The optimal set of features was selected with ReliefF,which helps improve the diagnosis accuracy.Support vector machine(SVM),which is suitable for a small sample,is adopted to realize diagnosis.The diagnosis accuracy of the proposed method reaches 100%,and its effectiveness is verified by experiment comparisons.In this paper,vibration signals are creatively adopted for fault diagnosis of railway point machines.The presented method can help guide field maintenance staff and also provide reference for fault diagnosis of other equipment.

Modified multi-scale symbolic dynamic entropy and fuzzy broad learning-based fast fault diagnosis of railway point machines

Railway point machine(RPM)condition monitoring has attracted engineers’attention for safe train operation and accident prevention.To realize the fast and accurate fault diagnosis of RPMs,this paper proposes a method based on entropy measurement and broad learning system(BLS).Firstly,the modified multi-scale symbolic dynamic entropy(MMSDE)module extracts dynamic characteristics from the collected acoustic signals as entropy features.Then,the fuzzy BLS takes the above entropy features as input to complete model training.Fuzzy BLS introduces the Takagi-Sug eno fuzzy system into BLS,which improves the model’s classification performance while considering computational speed.Experimental results indicate that the proposed method significantly reduces the running time while maintaining high accuracy.

Natural disaster warning system for safe operation of a high-speed railway

In this paper,the least square support vector machine(LSSVM)is used to study the safety of a high-speed railway.According to the principle of LSSVM regression prediction,the parameters of the LSSVM are optimized to model the natural disaster early warning of safe operation of a high-speed railway,and the management measures and methods of high-speed railway safety operation under natural disasters are given.The relevant statistical data of China’s high-speed railway are used for training and verification.The experimental results show that the LSSVM can well reflect the nonlinear relationship between the accident rate and the influencing factors,with high simulation accuracy and strong generalization ability,and can effectively predict the natural disasters in the safe operation of a high-speed railway.Moreover,the early warning system can improve the ability of safety operation evaluation and early warning of high-speed railway under natural disasters,realize the development goals of high-speed railway(safety,speed,economic,low-carbon and environmental protection)and provide a theoretical basis and technical support for improving the safety of a high-speed railway.

Research on the comfort degradation mechanism of high-speed railway passengers:a field study using dynamic interface pressure

Discomfort caused by long-term sitting decreases the passenger experience and may lead to musculoskeletal diseases, and this hasbecome one of the main problems for passengers of high-speed railways. However, the comfort degradation mechanism during longtermsitting in high-speed railways is still unknown. This study aimed to reveal passengers’ sitting comfort degradation mechanismin high-speed railways. By carrying out long-term sitting tests on high-speed trains running on the Shanghai-Kunming line, the dynamicinterface pressure and subjective comfort including overall and regional comfort of seven participants were obtained.Machinelearning models and statistical analysis methods were combined for data analysis to reveal the effect of regional comfort and thecontribution of sitting duration during the process of sitting comfort degradation. The results show that overall comfort is most significantlyinfluenced by the comfort of the shoulders, waist and buttocks. The seats play different roles before and after 20 minutesduring long-term sitting and there is a lag between the fatigue occurring and being offset. Therefore, the structure of seats affectsoverall comfort by affecting important regional comfort, and a long-term sitting test is necessary for accurate seat assessment. Thecomfort degradation mechanism can be used to define standards for long-term sitting comfort or provide guidance for seat evaluation,and the design and evaluation plan mentioned in this article for second-class seats can be applied to other cases with limitedaccommodating space.