scholarly journals Data-Driven Bias Correction and Defect Diagnosis Model for In-Service Vehicle Acceleration Measurements

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 872 ◽  
Author(s):  
Lei Bai ◽  
Rengkui Liu ◽  
Qing Li
Keyword(s):  
Bias Correction ◽  
False Positive ◽  
Measurement Data ◽  
Data Driven ◽  
Railway Track ◽  
Proposed Model ◽  
Vehicle Acceleration ◽  
Defect Diagnosis ◽  
Quality Instruments ◽  
Track Quality

Track quality instruments use low-cost accelerometers placed on or attached to the floors of operating trains, and these instruments collect substantial amounts of data over short inspection periods. The measurements collected by the instruments are the main data source for track irregularity evaluation. However, considerable measurement bias exists in the vertical and lateral vibration data obtained from such instruments. False positive track vibration defects detected by track quality instruments occur frequently. This results in considerable time and effort being expended needlessly because maintenance workers have to visit the railway track sites to check and review the track vibration defects. Therefore, we propose a model for data-driven bias correction and defect diagnosis for in-service vehicle acceleration measurements based on track degradation characteristics. Substantial amounts of historical track measurement data from different inspection methods were mined extensively to eliminate the false positive detection of track vibration defects and diagnose the causes of track vibration defects. Actual measurement data from the Lanxin Railway were used to validate our proposed model. The success rate achieved in identifying false positive track vibration defects was 84.1%, and that in track vibration defect diagnosis was 75.8%. These high success rates suggest that the proposed model can be of practical use in improving railway track maintenance management.

scholarly journals Condition monitoring of railway track systems by using acceleration signals on wheelset axle-boxes

Transport ◽  
2017 ◽  
Vol 33 (2) ◽  
pp. 555-566 ◽  
Author(s):  
Andrzej Chudzikiewicz ◽  
Roman Bogacz ◽  
Mariusz Kostrzewski ◽  
Robert Konowrocki
Keyword(s):  
Service Use ◽  
Preliminary Investigation ◽  
Measurement Data ◽  
Cost Effective ◽  
Railway Track ◽  
Track Condition ◽  
Preliminary Preparation ◽  
Multiple Unit ◽  
Acceleration Signals ◽  
Track Quality

The aim of this paper is to demonstrate the possibilities of estimating the track condition using axle-boxes and car-bodies motions described by acceleration signals. In the paper, the results presented indicate the condition of tracks obtained from the preliminary investigation on the test track. Furthermore, the results from the supervised runs (on Polish Railway Lines) of Electric Multiple Unit (EMU-ED74) with the prototype of track quality monitoring system installed on-board are described. As Track Quality Indicator (TQI) algorithm, used in the mentioned prototype, a modified Karhunen–Loève transformation is used in preliminary preparation of acceleration signals. The transformation is used to extract the principal dynamics from measurement data. Obtained results are compared to other methods of evaluating the geometrical track quality, namely methods, which apply the synthetic coefficient Jsynth and five parameters of defectiveness W5. The results from the investigation showed that track condition estimation is possible with acceptable accuracy for in-service use and for defining cost-effective maintenance strategies.

scholarly journals Analyzing Major Track Quality Indices and Introducing a Universally Applicable TQI

2020 ◽  
Vol 10 (23) ◽  
pp. 8490
Author(s):  
Stefan Offenbacher ◽  
Johannes Neuhold ◽  
Peter Veit ◽  
Matthias Landgraf
Keyword(s):  
Measurement Data ◽  
Railway Track ◽  
Quality Indices ◽  
Combine Measurement ◽  
Track Geometry ◽  
University Of Technology ◽  
Track Condition ◽  
Railway Operation ◽  
Selection Of ◽  
Track Quality

Adequate railway track condition is a prerequisite for safe and reliable railway operation. Many track quality indices (TQIs) have been developed with the aim of assessing the track condition holistically. These indices combine measurement signals of some or all relevant geometry parameters with different mathematical models. In this paper, a selection of important TQIs is evaluated. Using measurement data of a five kilometer track section, the indices are calculated and their properties are discussed. This study reveals that all indices exhibit drawbacks to varying degrees. As a consequence, a new index has been developed—the track quality index of Graz University of Technology (TUG_TQI). Its favorable characteristics are presented by means of the above-mentioned test section. The TUG_TQI combines all relevant track geometry parameters, which are normalized beforehand to eliminate over or underrepresentation of different parameters. Thus, the index reliably describes the overall geometrical track quality.

scholarly journals Track grid health index for grid-based, data-driven railway track health evaluation

2019 ◽  
Vol 11 (11) ◽  
pp. 168781401988976 ◽  
Author(s):  
Qing Li ◽  
Qiyuan Peng ◽  
Rengkui Liu ◽  
Ling Liu ◽  
Lei Bai
Keyword(s):  
Accurate Determination ◽  
Measurement Data ◽  
Evaluation Index System ◽  
Railway Track ◽  
Health Evaluation ◽  
Health Index ◽  
Condition Evaluation ◽  
Proposed Model ◽  
Bayes Algorithm ◽  
Adjacent Segments

Railway managers must have accurate assessments of railway track health to optimize maintenance and replacement scheduling and allocate resources reasonably. A model for railway track health evaluation, in which a continuous track line is divided into adjacent segments of the same length, referred to as track grids, is proposed in this study. A condition-evaluation index system for track grids was established, and deep autoencoder networks were used to reduce the dimensions of data on multiple condition measures. The set of all possible health features of the track grids was obtained using the hybrid hierarchical k-means clustering method. The tree-augmented naïve Bayes algorithm was employed to obtain the track grid health index and evaluate the overall health of the track grids. The proposed model was verified using measurement data from the Lanxin Railway in China. The proposed model was found to be superior to conventional health evaluation methods used in railway management in China. These results will enhance railway management knowledge and enable accurate determination of track health on a smaller spatial scale.

scholarly journals Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook

Machines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 13
Author(s):  
Yuhang Yang ◽  
Zhiqiao Dong ◽  
Yuquan Meng ◽  
Chenhui Shao
Keyword(s):  
High Resolution ◽  
High Performance ◽  
Sampling Design ◽  
Three Dimensional ◽  
Measurement Data ◽  
Data Driven ◽  
Surface Measurement ◽  
Smart Manufacturing ◽  
Future Research ◽  
3D Surface

High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent development in measurement technologies has made it possible to acquire high-resolution three-dimensional (3D) surface measurement data, it is generally expensive and time-consuming to use such technologies in real-world production settings. Data-driven approaches that stem from statistics and machine learning can potentially enable intelligent, cost-effective surface measurement and thus allow manufacturers to use high-resolution surface data for better decision-making without introducing substantial production cost induced by data acquisition. Among these methods, spatial and spatiotemporal interpolation techniques can draw inferences about unmeasured locations on a surface using the measurement of other locations, thus decreasing the measurement cost and time. However, interpolation methods are very sensitive to the availability of measurement data, and their performances largely depend on the measurement scheme or the sampling design, i.e., how to allocate measurement efforts. As such, sampling design is considered to be another important field that enables intelligent surface measurement. This paper reviews and summarizes the state-of-the-art research in interpolation and sampling design for surface measurement in varied manufacturing applications. Research gaps and future research directions are also identified and can serve as a fundamental guideline to industrial practitioners and researchers for future studies in these areas.

scholarly journals A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting

2021 ◽  
Vol 9 (4) ◽  
pp. 383
Author(s):  
Ting Yu ◽  
Jichao Wang
Keyword(s):  
Lead Time ◽  
Wave Period ◽  
Data Driven ◽  
Lead Times ◽  
Proposed Model ◽  
Data Driven Approach ◽  
Mean Wave Period ◽  
Scattering Index ◽  
Gated Recurrent Unit ◽  
Methods Numerical

Mean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and even fewer have investigated it with spatial and temporal information. In this study, correlations between ocean dynamic parameters are explored to obtain appropriate input features, significant wave height (SWH) and MWP. Subsequently, a data-driven approach, the convolution gated recurrent unit (Conv-GRU) model with spatiotemporal characteristics, is utilized to field forecast MWP with 1, 3, 6, 12, and 24-h lead times in the South China Sea. Six points at different locations and six consecutive moments at every 12-h intervals are selected to study the forecasting ability of the proposed model. The Conv-GRU model has a better performance than the single gated recurrent unit (GRU) model in terms of root mean square error (RMSE), the scattering index (SI), Bias, and the Pearson’s correlation coefficient (R). With the lead time increasing, the forecast effect shows a decreasing trend, specifically, the experiment displays a relatively smooth forecast curve and presents a great advantage in the short-term forecast of the MWP field in the Conv-GRU model, where the RMSE is 0.121 m for 1-h lead time.

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

scholarly journals A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster

2018 ◽  
Vol 5 (9) ◽  
pp. 180889 ◽  
Author(s):  
Zhengqiu Zhu ◽  
Bin Chen ◽  
Sihang Qiu ◽  
Rongxiao Wang ◽  
Yiping Wang ◽  
...  
Keyword(s):  
Air Quality ◽  
Measurement Data ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Data Driven ◽  
Atmospheric Environment ◽  
Illustrative Case ◽  
Air Quality Monitoring ◽  
Detection Capability ◽  
Data Driven Approach

The chemical industry is of paramount importance to the world economy and this industrial sector represents a substantial income source for developing countries. However, the chemical plants producing inside an industrial district pose a great threat to the surrounding atmospheric environment and human health. Therefore, designing an appropriate and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution-controlling strategies and facilities. As monitoring facilities located at inappropriate sites would affect data validity, a two-stage data-driven approach constituted of a spatio-temporal technique (i.e. Bayesian maximum entropy) and a multi-objective optimization model (i.e. maximum concentration detection capability and maximum dosage detection capability) is proposed in this paper. The approach aims at optimizing the design of an AQMN formed by gas sensor modules. Owing to the lack of long-term measurement data, our developed atmospheric dispersion simulation system was employed to generate simulated data for the above method. Finally, an illustrative case study was implemented to illustrate the feasibility of the proposed approach, and results imply that this work is able to design an appropriate AQMN with acceptable accuracy and efficiency.

scholarly journals A Proposed Model for Detecting Learning Styles Based on Agent Learning

2016 ◽  
Vol 11 (10) ◽  
pp. 65 ◽  
Author(s):  
M S Hasibuan ◽  
L E Nugroho ◽  
P I Santosa ◽  
S S Kusumawardani
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Evaluation Model ◽  
Learning System ◽  
Learning Activities ◽  
Instructional Materials ◽  
Data Driven ◽  
Automatic Learning ◽  
Proposed Model ◽  
Agent Learning

A learning style is an issue related to learners. In one way or the other, learning style could assist learners in their learning activities if students ignore their learning styles, it may influence their effort in understanding teaching materials. To overcome these problems, a model for reliable automatic learning style detection is needed. Currently, there are two approaches in detecting learning styles: data driven and literature based. Learners, especially those with changing learning styles, have difficulties in adopting these two approach since they are not adaptive, dynamic and responsive (ADR). To solve the above problems, a model using agent learning approach is proposes. Agent learning involves performing activities in four phases, i.e. initialization, learning, matching and, recommendations to decide the learning styles the students use. The proposed system will provide instructional materials that match the learning style that has been detected. The automatics detection process is performed by combining the data-driven and literature-based approaches. We propose an evaluation model agent learning system to ensure the model is working properly.

scholarly journals An Intelligent Marshalling Model for Enterprise Station Freight Railway

2020 ◽  
Author(s):  
Junxia Guo ◽  
Gang Lu ◽  
Zili Xie ◽  
Jiawei Wen ◽  
Nanshan Xu
Keyword(s):  
Supply Chain ◽  
Economic Cost ◽  
Economic Benefits ◽  
Path Selection ◽  
Railway Track ◽  
Production Task ◽  
Train Station ◽  
Proposed Model ◽  
Energy Consuming

Railway marshalling and transportation is an important component of the production supply chain for large and medium-sized enterprises in China. Traditional inefficient manual-made marshalling plans usually are not optimal in time or energy consuming. An efficient method needs to be developed to find the optimal marshalling plans automatically. This paper mainly studies the railway train automatic marshalling in large and medium-sized enterprises in China. Based on the investigation at the train station of a certain enterprise, according to the railway track information, carriage information, and production task information, this paper designs the abstracted railway state definitions of the station. Then based on the state definitions, the scheduling rules, and the objective function of time cost and economic cost, this paper converts abstract scheduling instructions into a general railway automatic marshalling model which can be executed by computers. By introducing the greedy strategies into different situations to optimize the algorithm of tracks occupation, carriages selection and train path selection in the model, the planning efficiency can be improved while ensuring the economic benefits of the enterprises and the quality of the formation plan. The experimental results show that the proposed model can generate fewer marshalling plans and find the optimal one faster in most cases, which proves the feasibility and availability of the model.

scholarly journals Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data

2021 ◽  
pp. 147592172110448
Author(s):  
Xuyan Tan ◽  
Yuhang Wang ◽  
Bowen Du ◽  
Junchen Ye ◽  
Weizhong Chen ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Mechanical Analysis ◽  
Data Driven ◽  
Monitoring Data ◽  
Tunnel Structure ◽  
Mechanical Behaviors ◽  
Proposed Model ◽  
Full Face ◽  
Cross Test ◽  
Mechanical Constraint

Mechanical analysis for the full face of tunnel structure is crucial to maintain stability, which is a challenge in classical analytical solutions and data analysis. Along this line, this study aims to develop a spatial deduction model to obtain the full-faced mechanical behaviors through integrating mechanical properties into pure data-driven model. The spatial tunnel structure is divided into many parts and reconstructed in a form of matrix. Then, the external load applied on structure in the field was considered to study the mechanical behaviors of tunnel. Based on the limited observed monitoring data in matrix and mechanical analysis results, a double-driven model was developed to obtain the full-faced information, in which the data-driven model was the dominant one and the mechanical constraint was the secondary one. To verify the presented spatial deduction model, cross-test was conducted through assuming partial monitoring data are unknown and regarding them as testing points. The well agreement between deduction results with actual monitoring results means the proposed model is reasonable. Therefore, it was employed to deduct both the current and historical performance of tunnel full face, which is crucial to prevent structural disasters.

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