Measuring Dynamic Wheel Loads on Subways and Trams
Designing for Safer, Longer-Lasting Wheelsets
Subway and tram wheelsets are exposed to extreme dynamic forces in urban rail service. Track irregularities, switch crossings, and braking all introduce loads that directly impact safety, ride comfort, and service life. To design wheelsets that are durable, safe, and comfortable, engineers need real-world load data to get a precise understanding of these forces—especially for rubber-sprung wheelsets.
Challenges
Modern vehicle development relies on simulation models, which require real-world load data for validation. This validation is a challenge, as it requires reliable measurement technology capable of capturing all relevant forces and moments directly at the wheel, even under demanding conditions. Only with a solid data foundation can engineers optimize designs, validate simulations, and develope informed maintenance strategies.
Solution
The solution is a specially developed measurement wheel that records all relevant forces and moments directly at the wheel, using strain-gauge sensors and contactless telemetry. The data is sent to a powerful data acquisition system for recording and real-time analysis — creating a solid foundation for design, validation, and lifecycle optimization.
Operations
At the heart of our solution is a custom measurement wheel seemlessly integrated into the bogie. Operated during regular passenger service, the wheel is equipped with 16 precisely applied strain gauges (SGs) that measure forces and moments in radial, axial, and torsional directions. Additional temperature sensors on the rubber elements monitor thermal effects.
The sensor signals are transmitted wirelessly via telemetry to a stationary receiver. Data is transferred inductively at high rates and without interference. By placing the signal conditioning directly on the wheel, we minimize noise and significantly improve data quality.
Strain-Gauge Measurement Basics
Forces and moments acting on a component generate surface strains that can be detected by strain gauges. A conversion model translates these strains into actual loads.
Positioning of Strain Gauges
To capture all relevant load conditions, several strain gauges must be placed at strategically suitable positions. Full-bridge circuits are typically used, as they provide temperature compensation and sensitivity to bending or torsion.
Typical positions on the wheel body:
- Radial forces: SGs applied near the wheel tread or hub.
- Axial forces: SGs placed on areas subjected to lateral loads in curves (e.g., flange areas).
- Torsional moments: SGs applied at ±45° to the circumferential direction, typically on the wheel disc or web.
Mechanical Model and Influence Coefficients
A mechanical model (e.g., FEM or analytical) defines the relationship between applied forces/moments and the resulting strains at the SG positions.
System Calibration
Calibration establishes the basis for accurate load reconstruction during operation and is usually determined both experimentally and numerically:
- Experimental: The wheel is subjected to defined forces and moments in the laboratory. The associated SG signals are recorded and a calibration matrix is created.
- Numerical (e.g., FEM): A simulation model reproduces loads and strain distributions. The strains at the actual SG positions are extracted.
Online Decoding & Real-Time Analysis
During service, SG signals are continuously aquired and processed with the imc CRONOSflex DAQ system. Force and moment reconstruction is performed in real time using a calibrated conversion matrix. The Online FAMOS platform enables onboard analysis, including:
- Temperature Compensation: Incorporates sensor data to correct for thermal effects
- Signal Quality: Filters and smoothing applied directly during measurement
- Statistical Evaluation: Real-time computation of peak values, RMS, load spectra, and distributions (e.g., Rainflow)
- Online Classification: Continuous data condensed into compact load matrices for easier handling and direct usability
In addition, the DAQ system supports time-synchronized acquisition with GPS as well as vehicle and track data, allowing loads to be precisely mapped to track sections, infrastructure conditions, or maneuvers such as curves and braking.
Test Setup & Methodology
- Measurement wheel with integrated sensors: 16 strain gauges for forces and moments plus at least 4 temperature sensors
- Telemetry system: Contactless power supply and secure digital data transfer to imc CRONOSflex
- Data acquisition & analysis: Automated visualization and processing of raw data
Procedure
- Installation of the measurement wheel on the leading axle of a tram in regular passenger service.
- Continuous data collection under real operating conditions.
- Synchronous recording of all channels (radial, axial, torsional, thermal).
- Post-processing to identify load spectra, peak loads, and characteristic load profiles for curves, switches, and braking.
Key Considerations
- Calibration & Alignment: Accuracy depends on proper SG calibration. Errors can cause systematic deviations.
- Sensor Installation: Incorrect strain-gauge application can result in signal errors or failure.
- Electromagnetic Interference: Urban environments require robust telemetry and digital transmission.
- Thermal Effects: Continuous monitoring is essential to account for changes in rubber element properties and sensor behavior.
- Data Management: Large volumes of data require structured handling and automated analysis to deliver timely insights.
Benefits of the imc Solution
✅ Accurate load data from real-world operations
✅ Contactless, interference-free telemetry
✅ Real-time results with imc CRONOSflex & Online FAMOS
✅ Expandable with GPS, vehicle bus data, and more
✅ Directly usable load spectra and classifications
✅ Proven technology for urban rail applications
Conclusion
Measuring dynamic loads on subway and tram wheels provides indispensable insights for the rail industry. With strain-gauge-based measurement wheels, digital telemetry, and powerful real-time analysis, engineers gain realistic operating data that simulations alone cannot deliver. This enables accurate validation of models, optimized wheelset designs, and predictive maintenance strategies—leading to safer, more reliable, and cost-efficient urban rail vehicles.
