Abstract
Real-world measurements are always affected by noise, e.g., thermal noise. For accurate parameter estimation results based
on measured data, e.g., for sensitive coating thickness determination, the minimization of noise influences is essential. This
paper focuses on modeling the noise behavior of an eddy current measurement system for coated steel sheets, involving the
measurement of the excitation current and the induced pick-up voltage, followed by FFT analysis and fundamental wave
evaluation. From this, sheet metal parameters can be determined. The presented model describes the propagation of noise
from a noisy sinusoidal excitation current to the induced voltage in the pick-up coils. This noise model is successfully verified
by means of measurements and thus validated. Such a model is necessary for the development of a maximum likelihood
estimator for coating thickness. Furthermore, it can serve as a basis for the optimal design of an eddy current sensor system.
on measured data, e.g., for sensitive coating thickness determination, the minimization of noise influences is essential. This
paper focuses on modeling the noise behavior of an eddy current measurement system for coated steel sheets, involving the
measurement of the excitation current and the induced pick-up voltage, followed by FFT analysis and fundamental wave
evaluation. From this, sheet metal parameters can be determined. The presented model describes the propagation of noise
from a noisy sinusoidal excitation current to the induced voltage in the pick-up coils. This noise model is successfully verified
by means of measurements and thus validated. Such a model is necessary for the development of a maximum likelihood
estimator for coating thickness. Furthermore, it can serve as a basis for the optimal design of an eddy current sensor system.
| Original language | English |
|---|---|
| Title of host publication | SMM Turin, 08.-11.09.2025 |
| Publication status | Accepted/In press - Sept 2025 |
Fields of science
- 202027 Mechatronics
- 202011 Electrical machines
- 202 Electrical Engineering, Electronics, Information Engineering
- 202025 Power electronics
- 202009 Electrical drive engineering
JKU Focus areas
- Sustainable Development: Responsible Technologies and Management
- Digital Transformation