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Electronic Freight Car Inspection Recording and Application of Internet-of-Things (IoT) and Machine-to-Machine (M2M) Frameworks

[+] Author Affiliations
Matthew Cowan, Bryan Schlake

Penn State Altoona, Altoona, PA

Joseph Lieberman, Jacob Cimbalista

Penn State University, State College, PA

Paper No. JRC2018-6192, pp. V001T02A006; 9 pages
  • 2018 Joint Rail Conference
  • 2018 Joint Rail Conference
  • Pittsburgh, Pennsylvania, USA, April 18–20, 2018
  • Conference Sponsors: Rail Transportation Division
  • ISBN: 978-0-7918-5097-8
  • Copyright © 2018 by ASME


Freight railroad classification yards have been compared to large-scale manufacturing plants, with inbound trains as the inputs and outbound trains as the outputs. Railcars often take up to 24 hours to be processed through a railyard due to the need for manual inbound inspection, car classification, manual outbound inspection, and other intermediate processes. Much of the inspection and repair process has historically been completed manually with handwritten documents. Until recently, car inspections were rarely documented unless repairs were required. Currently, when a defect is detected in the yard, the railcar inspector must complete a “bad order” form that is adhered to each side of the car. This process may take up to ten minutes per bad order. To reduce labor costs and improve efficiency, asset management technology and Internet-of-Things (IoT) frameworks can now be developed to reduce labor time needed to record bad orders, increase inspection visibility, and provide the opportunity to implement analytics and cognitive insights to optimize worker productivity and facilitate condition-based maintenance. The goal of this project is to develop a low-cost prototype electronic freight car inspection tracking system for small-scale (short line and regional) railroad companies. This system allows car inspectors to record mechanical inspection data using a ruggedized mobile platform (e.g. tablet or smartphone). This data may then be used to improve inspection quality and efficiency as well as reduce inspection redundancy. Data collection will involve two approaches. The first approach is the development of an Android-based mobile application to electronically record and store inspection data using a smartphone or rugged tablet. This automates the entire bad order form process by connecting to IBM’s Bluemix Cloudant NoSQL database. It allows for the information to be accessed by railroad mechanical managers or car owners, anywhere and at any time. The second approach is a web-based Machine-to-Machine (M2M) system using Bluetooth low energy (BLE) and beacon technology to store car inspection data on a secure website and/or a cloudant database. This approach introduces the freight car inspection process to the “physical web,” and it will offer numerous additional capabilities that are not possible with the current radio frequency identification device (RFID) system used for freight car tracking. By connecting railcars to the physical web, railcar specifications and inspection data can be updated in real-time and be made universally available. At the end of this paper, an evaluation and assessment is made of both the benefits and drawbacks of each of these approaches. The evaluation suggests that although some railroads may immediately benefit from these technological solutions, others may be better off with the current manual method until IoT and M2M become more universally accepted within the railroad industry. The primary value of this analysis is to provide a decision framework for railroads seeking to implement IoT systems in their freight car inspection practices. As an additional result, the software and IoT source code for the mobile app developed for this project will be open source to promote future collaboration within the industry.

Copyright © 2018 by ASME



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