With the focus in the realm of automotive testing, process thermostats play a vital role in providing the required operating environment. These process thermostats, with the operating medium of an ethylene glycol-water ratio, play a crucial role in terms of controlling their thermal properties. With an emphasis on identifying the best preprocessing method for classifying these ratios, this study utilises a detailed comparison of statistical methods and the wrapper method-based Genetic Algorithm as a search method for the most relevant feature selections. Given the huge number of existing sensor parameters in the system, thereby emphasising the importance of feature selection criteria for effective analysis and model training. Furthermore, a random forest-based classifier is used with these parameters to predict the accurate ethylene glycol to water ratio.
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.