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Knueppel noted that SEPTA has started its move away from manual data management and into the realm of automated data collection and analysis, and is moving toward the next step of using data in a predictive manner, in five major focus areas:
As can be seen in Figure 1, Data Analytics is viewed by SEPTA as being fundamental to making improvements in all five of these focus areas. Knueppel went on to show how Big Data and corresponding Big Data tools and techniques are being applied within SEPTA. These data analytic tools have been developed internally and by several conference sponsors and presenters. Thus, for example, in the key conference areas of maintenance management for rolling stock and track, Knueppel gave specific application examples:
Real-time remote condition monitoring, with automatic notices of unusual conditions, such as the “TekTracking” System Proof of Concept Application for Remote Monitoring at 40th Street Trolley Portal.
Use of automated track inspection systems and associated analysis algorithms for Just-in-Time Replacement, such as the use of GREX’s “Aurora” system for wood tie replacement.
Use of centralized vehicle onboard diagnostic systems with remote access such as the Wi-Tronix Violet System purchased for SEPTA’s new locomotives.
Use of new-generation automated inspection systems to include Remote Bridge Monitoring, GPS-Enabled Drones (Above-Ground); GPS-Denied Drones (Under Ground); Head-End Video; Geometry Car; UT Testing; and Ground-Penetrating Radar.
In all cases, the goal is to convert Data into Action, as shown in Figure 2.
The more-than 25 technical presentations addressing Big Data issues in track, equipment and operations, followed the keynote speaker’s lead in addressing what is being done right now in the industry, with the increasing amount of data being collected in all aspects of the railway industry. There is an increasing use of Data Science, the interdisciplinary field using evolving analysis tools and techniques to extract knowledge or insights from data in various forms, either structured or unstructured. Associated Data Analytic tools are being integrated into a new structure, Rail Data Science as illustrated in Figure 3.
Rail Data Science, sometimes referred to as Railroad Big Data Analytics, has been divided into 9 basic steps as follows:
While most railways have developed the tools for extensive data collection, cleaning, storing, collating and integration remain major challenges as is illustrated in Figure 4 and Figure 5. Likewise, structuring the data in preparation for and in conjunction with the model development steps (Figure 5) represents a critical part of any effective data modeling activity.
Likewise, the analysis and modeling tools represent critical steps in the Data Analytics process. The range of such tools currently being used by railways, suppliers and researchers include predictive analytic tools such as Logistic Regression and Bayesian Inference to Machine Learning and Deep Learning techniques, Image Recognition, Blockchain Technology, Language Recognition, Text Analytics, etc. One non-traditional approach uses Text Analysis and Latent Semantic Analysis (LSA), a Language Recognition technique used to analyze relationships between sets of documents and the terms they contain, to look at rail safety data in a new way.
The use of data for improved operations, maintenance and safety was an ongoing theme for the conference. This included applications in all aspects of railroad operations to include track, rolling stock and transportation.
On the track side, use of data analytics addressed many of the key aspects of track maintenance and safety, ranging from rail wear prediction, broken rail safety, tie inspection, and prediction of track geometry degradation and associated risk of derailments. Several such presentations looked at using such tools as Logistic Regression analysis to forecast probability of degradation of track geometry as a function of supplemental measurement data that provides for increased prediction accuracy over the traditional traffic and MGT inputs. This is illustrated in Figure 6, which shows the range of additional input variables that can be introduced using such Data Analytic techniques, and Figure 7, which presents an example output showing the probability of developing a geometry defect as a function of several of these key input variables.
Likewise, use of data analytics for addressing both transportation and rolling stock (equipment) was discussed for a range of issues.
This included the use of Data Analytics to predict anomalous events as shown in Figure 8, and to address the effect of unplanned (anomalous) events on on-time performance as discussed in Figure 9.
Data Analytics is leading the way in the move from Corrective (Reactive) maintenance to Predictive (Preventive) maintenance. This is, in fact, the path that SEPTA’s Knueppel discussed and showed in Figure 1. However, helping in this move is the development of the concept of a “Digital Twin,” a digital replica of a living or non-living physical entity (Figure 10). By bridging the physical and the virtual world, data is transmitted seamlessly, allowing the virtual entity to exist simultaneously with the physical entity. The evolution of Data Analytics is moving toward the concept of a Digital Twin.
For example, in the case of track data, the static and dynamic data collected by the broad array of inspection tools currently available (and being implemented in the near future) are locational and as such can be referenced to a digital Asset Register. This is independent of whether data comes from measuring, monitoring, IoT or inspections and reports. These in turn can be brought together in a Digital Twin, allowing Maintenance Engineers to:
Thus, we continue to witness an evolution in data analysis (Data Analytics or Data Science) moving from Deep Learning, to the Internet of Things (IoT), to Cognitive Computing to the Digital Twin (Figure 11). This evolution is clearly evident in the presentation of each year’s Big Data in Railroad Maintenance conference, with each new conference providing new focus on what has been accomplished in the “mining” of the railroads’ Big Data and the implementation of data analytics to develop predictive models and tools for both maintenance and safety. The University of Delaware expects even more insightful information to be available in its Big Data 2020 conference, which will be held Dec. 16-17, 2020, at the University of Delaware Newark campus. For more information, contact Professor Allan M Zarembski at firstname.lastname@example.org.
Allan M Zarembski, Ph.D, PE FASME, Hon. Mbr. AREMA, is Professor of Practice and Director of the Railroad Engineering and Safety Program, Department of Civil and Environmental Engineering, University of Delaware-Newark.
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This article first appeared on www.railwayage.com
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