SCS Data Analysis to detect Wheel Flats

During the challenging winter months, the T1 train's wheel lock can damage tracks and disrupt passenger experience due to loud noises. As part of the Rail Vehicle Engineering (RVE) team, I play a pivotal role in a wheel flat investigation on Line 2, monitoring and analyzing the SCS train system biweekly. Utilizing tools like SCS and AURA, I extract and process 6000+ rows of data, organizing logs, structuring data into tables, and visualizing events against specific locations. My ongoing efforts have so far included two monitoring sessions, with a forecast of identifying 5-6 wheel-flat incidents monthly, ensuring timely alerts for prompt servicing.

Background: 

• During the heavy winter months, slippery railway conditions can lead to wheel lock during heavy braking of the T1 train.

• Wheel lock can damage the track, initiate derailment, and produce a loud, unpleasant thumping sound for passengers.

• Rail Vehicle Engineering (RVE) initiates a wheel flat investigation on Line 2 to identify low traction areas.

 Problem:

• Identify and mitigate the issues of wheel lock and associated track damage.

• My tasks are to monitor and analyze the SCS train system biweekly to understand the frequency and locations of wheel flats.

• Monitoring tools include the SMS and AURA systems for SCS event codes and wheel flats.

Actions:

• Investigation and Monitoring:

  • An ongoing investigation on Line 2 is identifying low traction areas, with Rail Vehicle Engineering (RVE) actively monitoring a designated T1 train (SCS Train) for related issues.

  • RVE tracks event codes pertaining to wheel flats, inspecting and analyzing flagged trains at Greenwood Yard, while operators are advised to maintain active SCS, with emergency bypass options.

• Data Extraction and Analysis:

  • I must prepare to extract data from the SCS system by gathering essential tools, confirming the SCS train's track number with the Carhouse, and accessing the VOBC with a laptop.

  • Data extraction involves downloading 2000+ logs from three even-numbered cars using specific software, swapping the CPU columns of the VOBC, and consolidating the data from all cars for analysis.

  • I conducted the SCS Data Analysis by organizing and processing 6000+ lines of data using a designated workbook. The data was structured into tables, sorted, and visualized through graphs to display events against specific locations.

Results:

• Ongoing data collection process with bi-weekly checks until April 2024.

• I have monitored the SCS train twice.

• Anticipate 5-6 wheel-flat incidents monthly during heavy winter months (November to March).

• Aim to swiftly notify the Transportation group about any trains with wheel flats for immediate servicing.