Deep convolutional neural networks for detection of rail surface defects

Faghih-Roohi, Shahrzad; Hajizadeh, Siamak; Núñez, Alfredo; Babuška, Robert; Schutter, Bart De

doi:10.1109/ijcnn.2016.7727522

Cited by 256 publications

(119 citation statements)

References 22 publications

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“…Hence, we consider to use ABA measurements (Li et al, 2008). To enhance the visualization, ABA measurements are combined with rail image videos (Jamshidi et al, 2017a,b;Faghih-Roohi et al, 2016). In our case study, the ABA measurement and rail video images are used to study rail surface defects; specifically squats, as they are costly for railway networks.…”

Section: Step 1: Intelligent Rail Conditions Monitoringmentioning

confidence: 99%

“…We use a DCNN model in order to automatically estimate from the ABA signals the defect severity throughout the tracks based on a big data analysis. For training the DCNN, based on previous results (Jamshidi et al, 2017a,b;Faghih-Roohi et al, 2016), we obtain a set of labelled images with their severity. The labels used from the images samples are on a scale from 0 to above 4 according to the severity level of the defects visible in the squats found by analysis of rail images.…”

Section: Step 1: Intelligent Rail Conditions Monitoringmentioning

confidence: 99%

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A decision support approach for condition-based maintenance of rails based on big data analysis

Jamshidi

Hajizadeh

et al. 2018

Transportation Research Part C: Emerging Technologies

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In this paper, a decision support approach is proposed for condition-based maintenance of rails relying on expert-based systems. The methodology takes into account both the actual conditions of the rails (using axle box acceleration measurements and rail video images) and the prior knowledge of the railway track. The approach provides an integrated estimation of the rail health conditions to support the maintenance decisions for a given time period. An expert-based system is defined to analyse interdependency between the prior knowledge of the track (defined by influential factors) and the surface defect measurements over the rail. When the rail health conditions is computed, the different track segments are prioritized, in order to facilitate grinding planning of those segments of rail that are prone to critical conditions. In this paper, real-life rail conditions measurements from the track Amersfoort-Weert in the Dutch railway network are used to show the benefits of the proposed methodology. The results support infrastructure managers to analyse the problems in their rail infrastructure and to efficiently perform a condition-based maintenance decision making. IntroductionThe increase in train traffic and axle loads affect the health conditions of railway infrastructure. Hence, efficient infrastructure monitoring and maintenance is among the major concerns of infrastructure managers in order to improve the performance of railway operations (Åhrén and Parida, 2009). As such, infrastructure health conditions should be monitored and considered in the maintenance decision making process. Effective management of infrastructure health conditions is crucial to guarantee the desired asset quality level (Parida and Chattopadhyay, 2007;Gandomi and Haider, 2015;Zywiel and Oberlechner, 2001). It also plays an important role in meeting the demands for the whole system performance when the infrastructure is upgraded e.g. when increasing traffic capacity, the maintenance regime should be adapted to avoid compromising safety and infrastructure health requirements. To keep the infrastructure system working at an effective level, a conditions-based maintenance system is required not only to consider the actual heath conditions but also evolution during the maintenance decision horizon (Jamshidi et al., 2017b;Li et al., 2014).Condition-based monitoring is used in railway infrastructures to estimate the actual health conditions of the assets, so that degradation processes can be effectively controlled. It helps to keep the infrastructure manager continually informed of the estimated health of the railway infrastructure. Condition-based monitoring is supposed to collect information that will allow an effective https://doi.

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Section: Step 1: Intelligent Rail Conditions Monitoringmentioning

confidence: 99%

Section: Step 1: Intelligent Rail Conditions Monitoringmentioning

confidence: 99%

A decision support approach for condition-based maintenance of rails based on big data analysis

Jamshidi

Hajizadeh

et al. 2018

Transportation Research Part C: Emerging Technologies

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“…To be able to automatically extract defect information from the data, we train and apply a DCNN to detect and classify the defects. Recently, application of DCNN has become very popular in the domain of big data due to the increases in the size of available training sets and algorithmic advances such as the use of piece‐wise linear units and dropout training .…”

Section: Failure Risk Assessment Modelmentioning

confidence: 99%

A Big Data Analysis Approach for Rail Failure Risk Assessment

et al. 2017

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Railway infrastructure monitoring is a vital task to ensure rail transportation safety. A rail failure could result in not only a considerable impact on train delays and maintenance costs, but also on safety of passengers. In this article, the aim is to assess the risk of a rail failure by analyzing a type of rail surface defect called squats that are detected automatically among the huge number of records from video cameras. We propose an image processing approach for automatic detection of squats, especially severe types that are prone to rail breaks. We measure the visual length of the squats and use them to model the failure risk. For the assessment of the rail failure risk, we estimate the probability of rail failure based on the growth of squats. Moreover, we perform severity and crack growth analyses to consider the impact of rail traffic loads on defects in three different growth scenarios. The failure risk estimations are provided for several samples of squats with different crack growth lengths on a busy rail track of the Dutch railway network. The results illustrate the practicality and efficiency of the proposed approach.

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“…So this outstanding achievement of results reflects that this automated system can effectively replace manual ceramic tile detection system with better accuracy and efficiency. In 2016, S. Faghih-Roohi et al [4] proposed a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. This huge amount of data obtained from many hours of automated video recordings makes it impossible to manually inspect the images and detect rail surface defects.…”

Section: Introductionmentioning

confidence: 99%

Study on Machine Learning Based Intelligent Defect Detection System

Huang¹,

Lin

2018

MATEC Web Conf.

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In the paper, it is proposed to develop a machine learning based intelligent defect detection system for metal products. The common machine vision system has the surface (stain, shallow pit, shallow tumor, scratches, Edge defects, pattern defects) detection, or for the processing of the size, diameter, diameter, eccentricity, height, thickness and other parts of the non-contact numerical parameters of detection. Considering the quality of the work piece and the defects of the standard, so for the quality of customized testing requirements, the study is the development of machine vision and machine learning metal products defect detection system, mainly composed of three procedures: Image preprocessing, training procedures and testing procedures. The system architecture consists of three parts: (1) Image preprocessing: we first use the machine vision. OPENCV to carry out the image pre-processing part of the product before the detection. (2) Training procedures: The algorithm of the machine learning includes the convolution neural network (CNN), chunk-max pooling is used to train the program, and the generative adversarial network (GAN) based architecture is used to solve the problem of small datasets for surface defects. (3) Testing procedures：The Python language is used to write the program and implement the testing procedures with the GPU-Based embedded hardware In industries, collecting training dataset is usually costly and related methods are highly dataset-dependent. So most companies cannot provide Big-data to be analyzed or applied. By the experimental results, the recognition accuracy can be obviously improved as increasing data augmentation by GAN-Based samples maker. Manual inspection is labor intensive, costly and less in efficiency. Therefore, this study will contribute to technological innovation, industry, national development and other applications. (1) The use of intelligent machine learning technology will make the industry 4.0 technology more sophisticated. (2) It will make the development of equipment industry be better by the machine learning applications. (3) It will increase the economics and productivity of countries for the aging of the population by machine learning.

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Deep convolutional neural networks for detection of rail surface defects

Cited by 256 publications

References 22 publications

A decision support approach for condition-based maintenance of rails based on big data analysis

A decision support approach for condition-based maintenance of rails based on big data analysis

A Big Data Analysis Approach for Rail Failure Risk Assessment

Study on Machine Learning Based Intelligent Defect Detection System

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