Defect detection performance of a high-speed rail inspection system from passive acoustic identification

2020 ◽  
Author(s):  
Diptojit Datta ◽  
Albert Liang ◽  
Ranting Cui ◽  
Francesco Lanza di Scalea
Keyword(s):  
Defect Detection ◽  
High Speed ◽  
Detection Performance ◽  
Inspection System ◽  
High Speed Rail ◽  
Rail Inspection ◽  
Acoustic Identification ◽  
Passive Acoustic

Predictions of defect detection performance of air-coupled ultrasonic rail inspection system

2017 ◽  
Vol 17 (3) ◽  
pp. 684-705 ◽  
Author(s):  
Stefano Mariani ◽  
Francesco Lanza di Scalea
Keyword(s):  
Field Test ◽  
Defect Detection ◽  
Guided Waves ◽  
Numerical Study ◽  
Detection Performance ◽  
Probability Of Detection ◽  
Inspection System ◽  
False Alarms ◽  
Operating Variables ◽  
Rail Inspection

A rail inspection system based on ultrasonic guided waves and non-contact (air-coupled) ultrasound transduction is under development at the University of California at San Diego. The system targets defects in the rail head that are major causes of train accidents. Because of the high acoustic impedance mismatch between air and steel, the non-contact system poses severe challenges and questions on the defect detection performance. This article presents an extensive numerical study, conducted with a local interaction simulation approach, to model the ultrasound propagation and interaction with defects in the proposed system. This model was used to predict the expected detection performance of the system in the presence of various defects of different sizes and positions, and at varying levels of signal-to-noise ratios. When possible, operating variables for the model were chosen consistently with the field test of an experimental prototype that was conducted in 2014. The defect detection performance was evaluated through the computation of receiver operating characteristic curves in terms of probability of detection versus probability of false alarms. The study indicates that despite the challenges of non-contact probing of the rail, quite satisfactory inspection performance can be expected for a variety of defect types, sizes, and positions. Beyond the specific cases examined in this article, this numerical framework can also be used in the future to examine a larger variety of field test conditions.

scholarly journals Computer vision–based automatic rod-insulator defect detection in high-speed railway catenary system

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141877394 ◽  
Author(s):  
Ye Han ◽  
Zhigang Liu ◽  
DJ Lee ◽  
Wenqiang Liu ◽  
Junwen Chen ◽  
...  
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
High Speed ◽  
Power Transmission ◽  
Estimation Algorithm ◽  
Inspection System ◽  
High Speed Railway ◽  
Inspection Method ◽  
Railway Systems ◽  
Catenary System

Maintenance of catenary system is a crucial task for the safe operation of high-speed railway systems. Catenary system malfunction could interrupt railway service and threaten public safety. This article presents a computer vision algorithm that is developed to automatically detect the defective rod-insulators in a catenary system to ensure reliable power transmission. Two key challenges in building such a robust inspection system are addressed in this work, the detection of the insulators in the catenary image and the detection of possible defects. A two-step insulator detection method is implemented to detect insulators with different inclination angles in the image. The sub-images containing cantilevers and rods are first extracted from the catenary image. Then, the insulators are detected in the sub-image using deformable part models. A local intensity period estimation algorithm is designed specifically for insulator defect detection. Experimental results show that the proposed method is able to automatically and reliably detect insulator defects including the breakage of the ceramic discs and the foreign objects clamped between two ceramic discs. The performance of this visual inspection method meets the strict requirements for catenary system maintenance.

Surface defect detection for high-speed rails using an inverse P-M diffusion model

Sensor Review ◽  
2016 ◽  
Vol 36 (1) ◽  
pp. 86-97 ◽  
Author(s):  
Zhendong He ◽  
Yaonan Wang ◽  
Feng Yin ◽  
Jie Liu
Keyword(s):  
Diffusion Model ◽  
Defect Detection ◽  
High Speed ◽  
Surface Defect ◽  
Nearest Neighbor ◽  
Inspection System ◽  
Content Type ◽  
Difference Image ◽  
Vision Inspection ◽  
Surface Defect Detection

Purpose – When using a machine vision inspection system for rail surface defect detection, many complex factors such as illumination changes, reflection inequality, shadows, stains and rust might inevitably deform the scanned rail surface image. This paper aims to reduce the influence of these factors, a pipeline of image processing algorithms for robust defect detection is developed. Design/methodology/approach – First, a new inverse Perona-Malik (P-M) diffusion model is presented for image enhancement, which takes the reciprocal of gradient as feature to adjust the diffusion coefficients, and a distinct nearest-neighbor difference scheme is introduced to select proper defect boundaries during discretized implementation. As a result, the defect regions are sufficiently smoothened, whereas the faultless background remains unchanged. Then, by subtracting the diffused image from the original image, the defect features will be highlighted in the difference image. Subsequently, an adaptive threshold binarization, followed by an attribute opening like filter, can easily eliminate the noisy interferences and find out the desired defects. Findings – Using data from our developed inspection apparatus, the experiments show that the proposed method can attain a detection and measurement precisions as high as 93.6 and 85.9 per cent, respectively, while the recovery accuracy remains 93 per cent. Additionally, the proposed method is computationally efficient and can perform robustly even under complex environments. Originality/value – A pipeline of algorithms for rail surface detection is proposed. Particularly, an inverse P-M diffusion model with a distinct discretization scheme is introduced to enhance the defect boundaries and suppress noises. The performance of the proposed method has been verified with real images from our own developed system.

The Effect of Motion-Induced Eddy Currents on Three-Axis MFL Signals for High-Speed Rail Inspection

2021 ◽  
Vol 57 (4) ◽  
pp. 1-11
Author(s):  
Guanyu Piao ◽  
Jiaoyang Li ◽  
Lalita Udpa ◽  
Satish Udpa ◽  
Yiming Deng
Keyword(s):  
High Speed ◽  
Eddy Currents ◽  
High Speed Rail ◽  
Rail Inspection

A DEFECT DETECTION SCHEME FOR WEB SURFACE INSPECTION

2000 ◽  
Vol 14 (06) ◽  
pp. 735-755 ◽  
Author(s):  
JUKKA IIVARINEN ◽  
KATRIINA HEIKKINEN ◽  
JUHANI RAUHAMAA ◽  
PETRI VUORIMAA ◽  
ARI VISA
Keyword(s):  
Defect Detection ◽  
High Speed ◽  
Texture Features ◽  
Inspection System ◽  
Surface Inspection ◽  
Self Organizing Map ◽  
Detection Scheme ◽  
Web Inspection ◽  
Inspection Systems ◽  
Self Organizing

The goal of this work was to develop an improved defect detection scheme for high-speed real-time web surface inspection. This goal was realized by splitting the task into two independent parts: feature extraction and segmentation. Both parts were implemented using efficient algorithms which were implemented in hardware that is suitable and fast enough to be included in a working web inspection system. The proposed scheme is based on some derived texture features and a new self-organizing map variant, the statistical self-organizing map. These techniques offer several improvements over the gray-level thresholding techniques that have been traditionally used in commercial web inspection systems.

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