Mr. Andrew B. Off, P.E., PMP Assistant Chief Engineer, Track Washington Metropolitan Area Transit Authority (WMATA) 3424 words

Transcription

1 A comprehensive and integrated approach to MOW planning: Leveraging the power of linear asset management tools, track inspector assessments and automated track inspection data Mr. Kevin P. Moore President, The Net Consulting Group, Inc. Mr. Andrew B. Off, P.E., PMP Assistant Chief Engineer, Track Washington Metropolitan Area Transit Authority (WMATA) 3424 words Abstract Transits and railroads collect vast amounts of data with the intent of analyzing and managing the Maintenance-of- Way. One of the biggest challenges is developing a method to gather and analyze this disparate data efficiently and effectively. Many railways have terabytes of data including, but not limited to, track geometry, rail profile, contact rail geometry, catenary wire measurements, ultrasonic rail defects and track inspector defects. Managing this data and proactively prioritizing work is a tremendous challenge for all in the industry. This paper discusses how the Washington Metropolitan Area Transit Authority (WMATA) developed a method to merge and exploit condition and work data into one integrated system. This paper will describe the methods used to gather and store data as well as analyze and prioritize the work in a way that is most germane to the rail industry. The system and methodology discussed will demonstrate the need to allow for new condition data types while utilizing an extensible technology framework. Introduction Transits and railroads collect vast amounts of data each year. The industry is challenged with developing sustainable methods to harness this data into actionable information. Harnessing the data generally requires deploying an enterprise data system that is integrated with the railroads critical data sources. Once the data is aggregated into a central data system, the data can be analyzed to create actionable information. According to the Federal Transit Administration s (FTA) 2010 National State of Good Repair (SOGR) Assessment, roughly one third of transit assets are in a marginal to poor state of repair. Track and Structure was classified as the asset type with largest total value of marginal to poor assets. These assets were reported at approximately $80 Billion of value, shown in figure 1 below. The backlog of repair work necessary to bring these assets back into a state of good repair is significant and certainly exceeds the amount of funds available to replace these components. This represents an industry challenge that acutely heightens the need for organizing and aggregating data into actionable information so that the industry s limited resources are allocated using a prioritized and risk-based model. This paper explains the system and methods WMATA and The Net Consulting Group (TNCG) used to address this challenge, which further enhanced WMATA s proactive maintenance efforts and optimized capital improvement plans. These efforts primarily focused on Track and Structure asset types and targeted the areas in most critical need. AREMA

2 Figure 1 (FTA SOGR Backlog, 2010) Industry Challenge Maintenance officials making right-of-way (ROW) decisions and reporting upon state of good repair need to draw upon many data sources. These data sources are generally not easily utilized. There are a number of factors that inhibit railroads and transits from getting the most value out of the data they possess. Some of these challenges can be overcome with computer systems and some of the problems need to be addressed organizationally through policy changes. Some common challenges are: 1) Disparate Data: Not all of the railway data is centralized. In some cases the highest quality data can be found on a single workstation or in a single spreadsheet that is being maintained but not distributed. This data is sometimes printed for distribution and may not be cataloged and stored electronically in a centralized database. 2) Stand-alone Systems/Databases: Railways often use a number of stand-alone software systems with minimal data integration capabilities between them. Integrating the following data types are critical for executing proactive maintenance and capital planning along the right-of-way. a. Autonomous condition assessment vehicles, which may include i. Track geometry data ii. Rail profile data iii. Traction power geometry (third rail/catenary wire) iv. Ultrasonic Testing This data is often delivered by third parties via paper or a proprietary software package that cannot easily integrate with other key software packages. b. Work Order Management systems in a railway environment often include the following fundamental information: i. Track Inspection findings ii. Work orders specifying where and when to work iii. The work status progression from a work request to work complete iv. The cost of the work AREMA

3 c. Drawn right-of-way schematics which may include GIS data, corridor characteristics and asset data for all railway departments (i.e. Track, ATC, Traction Power, etc.): 3) Software Challenges: i. Vertical profile ii. Horizontal alignment iii. Structure type (aerial, at grade, tunnel) iv. Numerous other elements based on the end users preference a. Autonomous condition assessment systems are often stand-alone systems without enterprise level integration capabilities. b. Enterprise Asset Management (EAM) systems are often complex and require significant customization to accommodate the unique needs of the railroad and transit environment. EAM systems often struggle to handle linear assets and they are not designed to absorb continuous linear measurement data from autonomous condition assessment vehicles. c. Right of way schematics are often written in a software system that is designed to print. It is not easy to interact with this data online or to utilize this data for analysis. Since it is costly and burdensome to update these schematics, they are often not kept current. WMATA S Environment and Challenge: Data is plentiful, but knowledge is scarce. In its simplest terms, this fundamental reality served as both WMATA s challenge and catalyst for action. When were ties last replaced in that location? How many and what type of rail defects are at this location? The geometry car picked-up wide gauge in that area, are there any fastening system defects in the same location? We have a work block from X to Y this weekend, what other defects can we knock out while we have the track? How much head wear was at this location when we ran the geometry car last year? Where and how much rail do we need to replace next year? This list of hypothetical, yet practical, questions goes on and on. These scenarios are not unique to WMATA and happen on a daily basis at transit properties and railroads all over the globe. Eight s, three phone calls, two texts and four days later, you have your answer. At that point, it is likely that the wrong decision has already been made. And not unlike many other properties, in order to ensure that WMATA delivers a safe and reliable railroad, WMATA collects a full suite of automated track condition assessment data to include geometry, rail profile, lateral load, tie density scans, rail base corrosion and internal rail defects. In addition, WMATA s track inspectors walk every foot of mainline railroad two times per week. In short, we have the data. The challenge lies in coordinating, organizing, presenting and archiving the data in a manner which enables maintenance managers to make the right resource allocation decision at the right time. WMATA s track linear asset management philosophy is built on three fundamental pillars, all of equal importance to the state of good repair program: Automated Track Inspection Technologies; Track Walker Defect Work Orders and Corrective Maintenance Work Orders; and the Track Asset Register. 1) Automated Track Inspection Technologies In 2012, WMATA commissioned a Track Geometry Vehicle (TGV) to collect track condition assessment data. The system collects: Track Geometry Data Ultrasonic Testing (Internal Rail Defects) Third Rail Geometry Data AREMA

4 Platform Clearance Data Rail Profile Data Video Footage of the ROW Given that WMATA now owns, maintains and operates the TGV, WMATA has better control of the data and a greater range of flexibility to determine the frequency and location of testing activities. As noted previously, in addition to the above measurements, WMATA also contracts with service providers to deliver rail base corrosion, tie scanning and lateral load data. Further, there are many other automated testing technologies available to the industry to include ballast quality and high resolution joint bar photography, just to name a few. Regardless of how the data is collected, using either organic or contract assets, so long as the data is delivered in a common format (most commonly, a spreadsheet type format), the data can be loaded and analyzed in a comprehensive and holistic format. In general terms, if these technologies are only used and analyzed in isolation, the immediate benefit is identification and correction of a track condition that may otherwise serve as a safety hazard, which obviously adds value to the reliability and safety of the track infrastructure. However, this short-sighted approach will unlikely result in the identification of the proximate cause of the defect (the symptom ) and will certainly not result in a knowledge based capital program which is founded in a comprehensive assessment of all available information sources. 2) Work and Visual Inspection: WMATA s Track Department uses a fully automated EAM system to document all defects and work along the ROW. As would be expected, almost all work in the field is precipitated by a defect work order. Driven by department policy and business processes, all defect work orders are documented in the EAM system using an alpha-numeric coding system that accurately captures the specific track component, a detailed notation of the type of defect and a chain marker location reference. All components and defect types are assigned a unique component code, such as: Running Rail = R01 and D45 = Side Wear. Further, each defect is classified in terms of severity in accordance with the departments track standards manual. These standard business processes serve as the foundation of the departments defect management system and also readily facilitates the integration of track inspection defects and work completed data from the EAM system to WMATA s comprehensive track linear asset management system. 3) Assets Register: In 2012, WMATA was using paper track charts that were last updated in 2009, as shown in Figure 2 below. These charts were primarily in PDF format and distributed via paper throughout the organization. They contained the following data: Maintenance Record: Maintenance renewal activities for rail, fasteners and grinding General Information: Track network along with generally related information within the corridor Alignment Data: Design curve data for both mainline tracks Type of Structure: Structure characteristics along the corridor Profile: Grade characteristics along the corridor Traction Power and Train Control: Traction power and train control data along the corridor AREMA

5 Figure 2 (WMATA Hardcopy Track Charts, 2009) Prior to this effort, the three pillars of this program were viewed and analyzed in isolation. Track walker defects and work order information was viewed and analyzed in either the EAM system or using EAM system generated reports. The assets were viewed using PDF based track charts in either hardcopy or on a personal computer. Automated inspection data was analyzed by either simply reading the hardcopy printout of the data or viewing the data in a software platform delivered by the third party contractor whom collected the data. Herein lied the challenge how to find a way to organize and compile this data into a centralized system that enabled analysis and forecasting in a comprehensive and holistic manner. The Solution: Fundamentally, the solution entailed aggregating the key data types into one central system using spatial coordinates as a common reference between systems. Spatial coordinates include line, track, station marker and station marker AREMA

6 offset. From this common basis, all data can be associated and analyzed in a holistic format. The resulting MOW data system is often referred to as corridor infrastructure management (CIM). WMATA s proposed CIM model is shown in figure 3 below. CIM has been defined as the practice of using information for managing a safe, reliable and cost-efficient transportation corridor. Linear Asset Management, Linear Asset Maintenance, Analysis and Forecasting are all central tenets in a well-designed CIM system. Our solution was succinctly recognized and forecasted by the FTA in 2012 in report 0027: there must also be a system in place to record information and evaluate historical data. Location-based data from in-person and vehicle based inspections should be matched to track asset inventory data, usually divided into track segments identified by mileposts. Together with location-based maintenance data, the location-based condition data are the foundation of a performance-based track maintenance program. It is recommended that the data be hung on a track chart with the appropriate levels of asset inventory. Wayside sensors contribute by helping the engineering staff improve their modeling of the physical processes driving track wear. Transit agencies can use inspection data to introduce risk-based scoring into their prioritization process to better allocate maintenance resources. Figure 3 (WMATA Proposed CIM Model) Integrating this data into one centralized system allowed for a new level of proactive maintenance and capital planning by focusing on the areas of the corridor that need repair or replacement most. Once the data is centralized, railway analytics are applied to the data and the resulting information is distributed electronically. Continuous measurement data and exception data is loaded into the CIM system at least two times a year. All historical condition data is maintained in the CIM system to allow for trending analysis. Inspection data and work orders flow into the CIM system in real-time when they are updated. Autonomous vehicle exceptions, run over run continuous measurements, track inspection exceptions and work data is correlated by location and then analyzed which produce actionable reports. Figure 4 below illustrates the high-level information technology (IT) architecture which TNCG modeled to support WMATA s CIM system. AREMA

7 Figure 4 (TNCG and WMATA IT Integration) The project was executed using the following steps: Step 1: Create the asset register and display the corridor characteristics graphically. WMATA configured the CIM system using the same layout and symbols as used on their paper track charts. The asset register is a basic spreadsheet file which is then uploaded to Optram. Figure 5 (Optram Asset Register) AREMA

8 Step 2: Display work and track inspection data from the EAM system along with the asset register graphics. Key attributes passed to the CIM system include work location, work status, component codes, defect codes and severity. These component and defect codes may be further categorized to help facilitate SOGR reporting, analysis and forecasting. Figure 6 (Optram Asset Register and EAM Overlay) Also within Step 2, all historical component replacement data is displayed in CIM system (Optram). This data can be either loaded via spreadsheet or as part of the EAM integration. In figure 7, an example is shown for production work with the install date for key linear assets such as rail, fasteners, ties, and insulators. Figure 7 (Optram Component Replacement History) Step 3: Display continuous measurement data from the track inspection vehicles integrated with the asset register graphics. AREMA

9 Figure 8 (Optram Continuous Data and Asset Register Overlay) Step 4: Load run over run track conditon measurements and conduct analysis to determine the rate of degredation and forecast the need for repair or replacment. This data can also be folded into SOGR reports. This analysis is very helpful in producing curve degradation tables that can be used to best forecast curve replacement cycles and identify rail segments that are experiencing excessive degradation rates. In order to produce accurate analytics and forecasting the raw data needs to be aligned and processed. The following technique was used: 1. Plot two subsequent continues measurement runs 2. Align continuous data using a signature match algorithm 3. Calculate moving average 4. Plot difference to determine rate of change Figure 9 (Gauge Run Over Run Analysis in Optram) : AREMA

10 Figure 10 (Rail Gauge Face Angle Run Over Run Analysis in Optram) From Concept to Reality A Practical Example The below screen shots of the CIM system are intended to articulate how the CIM system can be used to produce a system-wide, performance based rail renewal plan. In general terms, wayside components (in this example, rails) are replaced on either a scheduled or unscheduled basis. Unscheduled component replacement is typically dictated by an emergency event (rail break, heat induced misalignment, derailment) or a finding which requires either taking the track out of service or ordering a speed restriction (internal defect, ultrasonic non-testable segments). Although not directly related, one of the primary functions of the CIM system is to reduce the occurrence of unscheduled maintenance. From a scheduled replacment perspective, rails are typically scheduled for replacement due to rail wear or rail surface defects which exceed or nearly exceed prescribed rail wear and rail surface condition standards. Understanding why track components are replaced will result in a detailed listing of data that is needed to make good component replacement decisions. In this case, we are exploring a rail replacement methodology; however, the same logic would apply to other track components such as, but not limited to, ties, ballast and fastening systems. In this example, the data is arranged to display a 13 mile rail segment. In this particular case, the following data is displayed: top wear, side wear, gauge face angle wear, rail head cant and track inspector rail surface defects. Any available data channel can be arranged and easily added dependent upon the end users preference. As an example, an end user may chose to add a data channel that displays all historical internal rail defects so one can include a rail defects per mile analysis to further enhance the rail renewal planning process. The permutations of data arrangement is only limited by the end users preference and the availability of data loaded to the CIM platform. Figure 11 below shows the noted data channels for the left rail of one track (13 miles long). At this macro-level, the end user can readily identify those data channels which exceed or nearly exceed prescribed standards which are noted by blue horizontal lines. In addition, the end user can see where track surface defects are located along this same segment of rail. In this example, two cases are examined by simply zooming-in to those specific segments. AREMA

11 Figure 11 (High Level Rail Renewal Analysis displaying entire line) Scenario #2 rail wear + surface cluster Scenario #1 rail wear Figure 12 below details a zoomed-in view of the area called out in scenario #1. In this case, the end user can draw meaningful conclusions regarding the condition of the rail. Those conclusions are summarized within the yellow box in figure 12. Figure 12 (Low Level Rail Renewal Analysis zooming into Scenario #1 area) Scenario #1 - drill down facts: gauge face angle (GFA) at limit, compound curve, no surface defects, need to restore positive railhead cant in high rail (left rail) AREMA

12 Figure 13 details a zoomed-in view of the area circled in figure 12 which is labeled as scenario #2. Again, the end user can readily comprehend the condition of the asset and deliver meaningful information to the department s maintenance crews for further investigation, as noted in the yellow box in figure 3. In addition, if the end user requires further information regarding the surface defects (noted as R01:D08 in figure 13), the end user can simply click on the surface defect and will be directed to that specific work order in the EAM system. Figure 13 (Low Level Rail Renewal Analysis zooming into Scenario #2 area) Scenario #2 - drill down facts: GFA erratic (below threshold), in leading spiral, repeat surface defects (EAM research), pass to track inspection and grinding project manager for action and feedback The iterative process noted above would continue for the rail segment of interest. In end state, the resulting deliverable is a prioritized rail renewal plan. Conclusion The industries movement towards new and more sophisticated condition assessment technologies will produce ever increasing quantities of data and data viewing systems. As these technologies evolve, processes and CIM systems must be adapted and developed so that transit agencies and railroads alike have the tools and means necessary to analyze these data sets in a comprehensive environment which will facilitate a more focused and proactive maintenance approach. Ultimately, these processes will optimize the allocation of the industries limited resources and deliver a safer and more reliable track infrastructure which is the overarching purpose of all MOW professionals. AREMA

13 References 1. Ted Selig, Corridor Infrastructure Management An Emerging Approach of Using Information for Improved Railway Safety, Reliability and Profitability, Bentley Systems White Paper, January 11, Theodore R. Sussmann, U.S. DOT/Volpe Center, Ted Selig, Optram, Inc. Vincent R. Terrill, Terrill Track Consultants, Ernest T. Selig, Ernest T. Selig, Inc. & Optram Inc., Enhanced Corridor Reliability Using Track Information System and Special Condition Indicators, International Heavy Haul Association, June Federal Transit Administration(FTA), Asset Management Guide, FTA Report No. 0027, October 2012 AREMA