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Norfolk, VA, United States

The Norfolk Southern Railway is a Class I railroad in the United States, owned by the Norfolk Southern Corporation. With headquarters in Norfolk, Virginia, the company operates over 22,000 route miles in 22 eastern states, the District of Columbia, and has rights in Canada from Buffalo to Toronto and over the Albany to Montreal route. The most common commodity hauled on the railroad is coal from mines in Indiana, Kentucky, Pennsylvania, Tennessee, Virginia, and West Virginia. The railroad also offers the most extensive intermodal network in eastern North America.The current system was formed in 1982 with the creation of the Norfolk Southern Corporation, a holding company, and on December 31, 1990, the Southern Railway was renamed the Norfolk Southern Railway, and control of the Norfolk & Western Railway was transferred from the holding company to the Norfolk Southern Railway. In 1999, the system grew substantially with the acquisition of over half of Conrail.Together Norfolk Southern Railway and CSX Railroad have a duopoly over all east-west freight rail traffic east of the Mississippi River.As of October 1, 2014 Norfolk Southern Railway's total public stock value was slightly over $34.5 billion. Wikipedia.

Cheong T.,Norfolk Southern Corporation | White III C.C.,Georgia Institute of Technology
IEEE Transactions on Intelligent Transportation Systems | Year: 2012

We investigate the value of choosing the next stop to visit in a multistop trip based on current traffic conditions to minimize the expected total travel time of the tour. We model this problem as a Markov decision process. Numerical examples are presented that indicate that the value of using current traffic information for dynamic tour determination can be significant. © 2011 IEEE.

Hong S.J.,Myongji University | Lim W.Y.,Myongji University | Cheong T.,Norfolk Southern Corporation | May G.S.,Georgia Institute of Technology
IEEE Transactions on Semiconductor Manufacturing | Year: 2012

A method of fault detection and classification (FDC) for semiconductor manufacturing equipment -diagnostics using equipment data is presented. Detecting faulty processes, identifying any anomaly at their onsets, and rapidly classifying the root cause of the fault are crucial for maximizing equipment utilization in current semiconductor manufacturing; however, tool data acquired from production equipment contains much information that is often challenging to analyze due to its sheer volume and complexity. In this paper, modular neural network (MNN) modeling is presented as a method for fault detection modeling in plasma etching. Based on the result from the MNN modeling, a tool data set is grouped according to its related subsystems, and FDC is performed using Dempster-Shafer (D-S) theory to address the uncertainty associated with fault diagnosis. Subsystem level fault detections, such as radio frequency (RF) power source module, RF power bias module, gas delivery module, and process chamber module, are presented by combining related parameters, and successful fault detection is achieved. The evidential reasoning of RF probe is also beneficial for the detection of chamber leak simulation, and the classification of fault is made by further investigating voltage signal of RF probe. Successful fault detection in subsystem level with zero missed alarms was demonstrated using D-S theory of evidential reasoning, and the classification for finding root cause of the fault is presented in the chamber leak fault simulation. We realized that successful FDC can be accomplished by combining various related information and by incorporating engineering expert knowledge. © 2011 IEEE.

Kwon C.,State University of New York at Buffalo | Lee T.,Chonbuk National University | Berglund P.,Norfolk Southern Corporation
Naval Research Logistics | Year: 2013

We consider a robust shortest path problem when the cost coefficient is the product of two uncertain factors. We first show that the robust problem can be solved in polynomial time by a dual-variable enumeration with shortest path problems as subproblems. We also propose a path enumeration approach using a K -shortest paths finding algorithm that may be efficient in many real cases. An application in hazardous materials transportation is discussed, and the solution methods are illustrated by numerical examples. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013 Copyright © 2013 Wiley Periodicals, Inc.

Norfolk Southern Corporation | Date: 2014-02-12

Embodiments of the present invention are directed to improved designs of railroad spikes and improved methods of manufacturing the same. According to one exemplary embodiment, a method for manufacturing a railroad spike may comprise the steps of: preparing a metal blank having a substantially circular cross-section; subjecting the metal blank to at least one cold heading process and at least one cold extrusion process to form a railroad spike having (a) a circular head with a fillet at its bottom side that is angled to engage a railroad tie plate or rail base and (b) a non-threaded shank with a substantially square cross-section and a chiseled tip; and coating the railroad spike with an anti-corrosion material.

Norfolk Southern Corporation | Date: 2014-05-30

A system and method for testing and maintaining a power assembly of a locomotive diesel engine. In one embodiment of the invention, the testing apparatus comprises: a first test position for securing a strongback of a power assembly; a second test position for securing a cylinder head of the power assembly; and a third test position for securing a cylinder liner. In another embodiment of the invention, the testing method comprises: securing a power assembly to a first test position; removing a cylinder head from the power assembly; securing the cylinder head to a second test position; removing a cylinder liner from the strongback assembly; placing the cylinder liner into the third test position; cleaning the cylinder liner; testing the cylinder head for possible leakage; and re-assembling the power assembly cylinder head passes the leakage tests.

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