Fast multidimensional scaling on big geospatial data using neural networks

The diagnosis of railway system faults is significant for its comfort, efficiency, and safety. The rail surface wear is the key impact factor when considering the health conditions of rails. This paper accomplishes contactless rail wear diagnosis by using multidimensional scaling based on a novel informational dissimilarity measure (IDM) to cluster intact and different worn rail profile data. The IDM uses weighted‐probability distribution of dispersion patterns to extract accurate time domain features from rail profile data, and the loss of information is minimized, which can greatly improve the accuracy for wear diagnosis. All the analyzing data for real experiments are collected by a laser scanner camera on an inspection car, where heavy‐haul railway rails with different types of surface wear are inspected. Experimental results with simulated and reality‐based data show that the proposed methods can identify worn profile data and discriminate different types of worn profiles more effectively when compared with existing methods. Thus, the proposed method offers a new thinking for the diagnosis of rail surface wear for heavy‐haul railways.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Fast multidimensional scaling on big geospatial data using neural networks

Cited by 2 publications

References 18 publications

A novel clustering method for complex signals and feature extraction based on advanced information-based dissimilarity measure

A novel clustering method for complex signals and feature extraction based on advanced information-based dissimilarity measure

Wear diagnosis for rail profile data using a novel multidimensional scaling clustering method

Contact Info

Product

Resources

About