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Many internal defects maybe arise in railway track working, which usually have different shapes and distribution rules. To solve the problem, an intelligent detection method is proposed for internal defects of railway track based on generalization features cluster in this paper. Firstly, defects are classified and counted according to their shapes and locations features. Then, generalized features of defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same types of defects. Finally, extracted generalized features are expressed by function constraints, and formulated as generalization feature clusters to classify and identify internal defects of the railway track. Furthermore, a reduced dimension method of the generalization features clusters is presented too in this paper. Based on the reduced dimension feature and strong constrained generalized features, the K-means clustering algorithm is developed for defects clustering, and good clustering results are achieved. To defects in the rail head region, its clustering accuracy is over 95%, and the Davies-Bouldin Index (DBI) index is small, which indicates the validation of the proposed generalization features with strong constraints. Experimental results show that accuracy of the proposed method based on generalization features clusters is up to 97.55%, and the average detection time is 0.12s/frame, which indicates it has good performance in adaptability, high accuracy and detection speed under the complex working environments.
Keywords
Railway track, Generalization features cluster, Defects classification, Ultrasonic image, Defect detection
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CURRENT STATUS: Under Revision
Version 1
Posted 08 Apr, 2021
No community comments so far
Editorial decision: Minor revision
On 28 Feb, 2022
Reviews received
Received 09 Nov, 2021
Reviewer #2 agreed
On 08 Nov, 2021
Review #1 received
Received 04 May, 2021
Reviewer #1 agreed
On 08 Apr, 2021
Reviewers invited
Invitations sent on 07 Apr, 2021
Editor invited
On 31 Mar, 2021
Editor assigned
On 19 Mar, 2021
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On 19 Mar, 2021
First submitted
On 18 Mar, 2021
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This preprint is under consideration at Chinese Journal of Mechanical Engineering. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Original Article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 08 Apr, 2021
No community comments so far
Editorial decision: Minor revision
On 28 Feb, 2022
Reviews received
Received 09 Nov, 2021
Reviewer #2 agreed
On 08 Nov, 2021
Review #1 received
Received 04 May, 2021
Reviewer #1 agreed
On 08 Apr, 2021
Reviewers invited
Invitations sent on 07 Apr, 2021
Editor invited
On 31 Mar, 2021
Editor assigned
On 19 Mar, 2021
Submission checks complete
On 19 Mar, 2021
First submitted
On 18 Mar, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Many internal defects maybe arise in railway track working, which usually have different shapes and distribution rules. To solve the problem, an intelligent detection method is proposed for internal defects of railway track based on generalization features cluster in this paper. Firstly, defects are classified and counted according to their shapes and locations features. Then, generalized features of defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same types of defects. Finally, extracted generalized features are expressed by function constraints, and formulated as generalization feature clusters to classify and identify internal defects of the railway track. Furthermore, a reduced dimension method of the generalization features clusters is presented too in this paper. Based on the reduced dimension feature and strong constrained generalized features, the K-means clustering algorithm is developed for defects clustering, and good clustering results are achieved. To defects in the rail head region, its clustering accuracy is over 95%, and the Davies-Bouldin Index (DBI) index is small, which indicates the validation of the proposed generalization features with strong constraints. Experimental results show that accuracy of the proposed method based on generalization features clusters is up to 97.55%, and the average detection time is 0.12s/frame, which indicates it has good performance in adaptability, high accuracy and detection speed under the complex working environments.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
The full text of this article is available to read as a PDF.
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