Test & Measurement
circumference of nearly 27 km. Since measurements are taken along the ring, a simple calculation shows that – assuming an idealized signal propagation time of 5 ns – it can take up to 135 µs for a measurement value to become available. The errors resulting from these spatial constraints were unacceptable.
To further improve the performance of the IEEE 1588 standard in this context, CERN independently extended the standard and published it as the “White Rabbit” standard under the CERN Open Hardware License. This standard is currently being used in various projects. CERN employs it for the control system of the “Large Hadron Collider” and for its KM3NeT neutrino telescope in the Mediterranean. The “GSI Helmholtz Centre for Heavy Ion Research” uses it for the FAIR particle accelerator. The accuracy of the White Rabbit implementation falls within the ±200 ps range, making it approximately 100 times more accurate than IEEE 1588 over Ethernet.
Time-related application areas Which scenarios require measurements with a corresponding time reference? As a first step, the following areas can be identified: Digital twin for simulation models Data basis for creating AI models Distributed measurements (long distances)
Time-synchronous triggers
The digital twin has gained particular significance for test and measurement (T&M) systems. It serves as a basis for simulation models or for the development of algorithms. A digital twin is a digital (virtual) representation of a real-world process and contains its data. In the early 21st century, the idea of the digital twin was further developed by M. Grieves and J. Vickers into a conceptual model consisting of three main parts: 1 Physical products in “real space” 2 Virtual or digital products in “virtual space”
3 The data and information connections linking the two
To utilize digital twin data, the data typically needs to be correlated using timestamps within the T&M (test and measurement) environment, enabling optimal use in simulation models. Specifically, discussions were held a few years ago with a university in Germany regarding the implementation of a digital twin to optimize welding processes in the automotive sector. The project involved using a measurement system to capture
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Picture 4: TSEP Herakles.Testbench: Clock configuration of a measurement scenario.
various parameters (such as voltage, current, electrode distance, wire feed rate, and temperature) during the welding process.
Another use case in the automotive sector could be an engine test bench. Typically, hundreds of measured values are generated in such test benches. If these values are timestamped, a corresponding simulation model can be created based on them. Such simulation models can potentially be used to optimize the number of actual tests performed on the test bench. If one consistently takes the concept of the digital twin a step further, one naturally arrives at AI models. These are trained using the data available from the digital twin. This approach would be applicable to both scenarios mentioned above. It would be particularly effective in the example of optimizing the welding process. Here, robots equipped with the corresponding AI models could improve the welding process in production.
Software
As the previous explanation indicates, the configuration effort for such systems is complex and substantial. These systems require a high degree of automation for both configuration and operation. For instance, when using a time-based system, the time distribution hierarchy – determining which unit provides the master clock and how time is distributed to the slave clocks – must be configured. It should also be noted that these systems require a certain amount of time to synchronize all clocks before measurement scenarios can begin. Furthermore, it must
be ensured that all measurement data are correctly timestamped and stored. Such systems should also be capable of monitoring and evaluating the quality of time synchronization during the measurement process. This ensures that synchronization errors do not affect the measurements (Picture 3).
It becomes clear that the test engineer who is working on this measurement scenario needs to be supported here. A graphical visualization of the configuration of the test system, the configuration of the clock distribution and the measured variables used as well as their parameters are absolutely necessary here. In the graphic shown (Picture 4), the test engineer can define both the configuration of the clock distribution as well as the measured variables used and their parameters. The software automatically configures the corresponding subcomponents and starts the system if necessary. Before a measurement can be carried out, all calibration data is set, the measured variables are configured and it is ensured that all clocks have been synchronized.
Calibration
In time-critical test systems, signal propagation times and the time required for sensors to provide the corresponding measured values are parameters that must be taken into account. The test engineer requires software support to determine these values. Defining the measurement scenario establishes the boundary conditions and key parameters. If the test engineer decides to record time-dependent
measurements, the software can guide them through the necessary configuration and calibration steps. This ensures that all required data is captured and made available to the measurement hardware.
Additionally, the time required for sensors to determine measured values can be obtained from the manufacturer’s data sheet or measured directly. If sensors or measuring instruments are connected via long cables (or trigger lines), signal propagation times must be measured. Various methods exist for measuring propagation time. The system described above employs a D-DMTD (Digital Dual Mixer Time Difference) algorithm to determine signal propagation times within the cables. Naturally, the relevant propagation times within the measurement system itself must also be known. This data must be provided by the system manufacturer and factored into the measurement process.
Conclusion
Time-based T&M systems make it possible to address new areas, particularly fields such as simulation and AI models. While these T&M systems are certainly more elaborate and complex, they are just as easy to operate as traditional T&M systems when paired with the appropriate software. Unfortunately, many T&M system manufacturers still focus on traditional systems – a trend that will undoubtedly shift as customer demand grows. There are already manufacturers who have recognized this potential and are consistently offering this option to their customers.
https://tsep.com/en Components in Electronics October 2026 37
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