Open Access

Akash Mangaluru Ramanandaa, Timo König, Fabian Wagner, Markus Kley

• In automotive testing, accurate measurement of torque and speed is critical. However, it is often expensive, requires precise mounting and positioning of sensors, a well as a controlled maintenance. The proposed approach focuses on overcoming these limitations by developing a novel concept of virtual sensors using vibration measurements as a replacement for physical and expensive sensors to measure input shaft speed and output torque with a case study of a two-stage reduction gearbox. The gearbox, mounted on a powertrain test rig, is equipped with multiple sensors, emphasizing sensor positioning and sensor combination as relevant points. A Random Forest-based regression feature importance (RFR-FI) method is used to identify the best performing sensors. A comprehensive time domain analysis and frequency domain comparison is also performed to select the optimal data for model training. Verification of the selected parameters and data is performed by a time-frequency domain analysis using spectrograms. Training two Artificial Neural Network (ANNR) models to predict input shaft speed and output shaft torque results in sufficiently accurate values for R2 and the Root Mean Squared Error (RMSE). The trained model is evaluated on unseen data with a randomized test cycle, which also yields good results in predicting torque and speed. This method highlights a cost-effective yet reliable estimation of critical parameters for automotive testing, emphasizing the effectiveness and precision of virtual sensor techniques.