Interface - The Journal of Wheel/Rail Interaction

WAYSIDE LOAD DETECTION

Using Wayside Load Detectors for Preventive Vehicle Maintenance
(Part 2 of 2)
By B. McGuire, R. Sarunac, R. B. Wiley and P. Klauser • April, 2007

Part 1 of this article examined ways in which the use of vehicle performance detectors is steadily growing on North American freight and high-speed passenger railways. With the installation of a Wayside Wheel/Rail Load Detector on the Washington Metropolitan Area Transit Authority, the trend now extends to the rail transit industry, as well.

The Washington Metropolitan Area Transit Authority (WMATA) engineering staff, working with a team of engineers from Booz Allen Hamilton and the Transportation Technology Center, Inc. (TTCI), installed a Wayside Wheel/Rail Load Detector (WRLD) to assess vehicle performance and identify poorly performing rail cars. WMATA’s primary objective was to characterize the existing fleet by establishing a database of wheel/rail forces. WMATA also wanted to monitor wheel/rail forces on vehicles that have experienced low-speed flange-climb derailments (and thereby decrease the probability of such incidents), establish a baseline of wheel/rail forces for the newest series rail cars, and compare measured wheel/rail forces with modeling and simulation results. A secondary objective was to pinpoint specific defects based upon the vehicle's dynamic signatures.

WMATA instrumented the high and low rails of a 1,614-foot radius curve without superelevation to measure vertical and lateral wheel forces, as well as axle angles of attack. An Automatic Equipment Identification (AEI) reader was installed at the same location to correlate data with specific vehicles. This allows tracking of individual rail cars, rail cars by series, and the fleet characteristics by rail car series. The WRLD acquisition system automatically collects the date and time of each passing car/train. It also collects vertical and lateral forces for each passing wheel, and the angle of attack of each passing axle. WRLD calculations include (but are not limited to) the speed of each train, the L/V ratio of each passing wheel, the net axle lateral for each axle, the truck side L/V for each passing truck, the axle sum L/V ratio for each passing axle, the average car weight, and the total train weight. Downloadable real-time data from the most recent 2000 trains is stored at the WRLD site. WMATA was most concerned about the system's ability to identify "troublemakers," or poorly performing vehicles. Preliminary data analysis and vehicle inspections confirmed that the systems did so.

Data collection begins as the detector determines that a train is approaching. Each wheel produces a force signature that is captured and held until the train passes the site. The force data is captured as medium frequency wave files (25 KHz), which are pre-processed to determine the peak values. These values represent the peak (lateral and vertical) forces that are generated by the wheel as it crosses the strain gauge sensor. AEI tags on the vehicles are read, and the data is merged with the force data to produce a train data set. Both the wave data and train data set are stored in the detector’s master computer.

The data is sent via the Internet to the TTCI for processing and loading into the InteRRIS® system. The data is scanned by an Event-Tracking™ engine to determine if any readings or performance indices have exceeded the established performance thresholds. If so, notifications are sent as e-mail or a direct data feed to the customer's internal systems.

System Calibration and Vehicle Monitoring
Establishing and generating alarms that identified poorly performing vehicles was critical to gaining maintenance personnel’s confidence in the WRLD’s capabilities. Performance alarms were based on single-wheel, high-rail L/V ratios and the number of occurrences.

The standard approach to developing alarm or alert levels is to follow an operations research-type analysis of the fleet’s performance data. The purpose is to fully characterize the performance in terms of the attributes of operating conditions, vehicle orientation and vehicle configuration. An exploratory analysis establishes standard descriptive statistics, frequencies and the probability distribution of all performance indices. The usual artifacts include a myriad of frequency and breakdown tables, categorized scatter, box and whisker plots, and categorized histograms (see Figures 1 and 2).

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