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The use of data-driven algorithms for the integration or substitution of current production sensors is becoming a consolidated trend in research and development in the automotive field. Due to the large number of variables and scenarios to consider; however, it is of paramount importance to define a consistent methodology accounting for uncertainty evaluations and preprocessing steps, that are often overlooked in naïve implementations. Among the potential applications, the use of virtual sensors for the analysis of solid emissions in transient cycles is particularly appealing for industrial applications, considering the new legislations scenario and the fact that, to our best knowledge, no robust models have been previously developed. In the present work, the authors present a detailed overview of the problematics arising in the development of a virtual sensor, with particular focus on the transient particulate number (diameter <10 nm) emissions, overcome by leveraging data-driven algorithms and a profound knowledge of the underlying physical limitations. The workflow has been tested and validated using a complete dataset composed of more than 30 full driving cycles obtained from industrial experimentations, underlying the importance of each step and its possible variations. The final results show that a reliable model for transient particulate number emissions is possible and the accuracy reached is compatible with the intrinsic cycle to cycle variability of the phenomenon, while ensuring control over the quality of the predicted values, in order to provide valuable insight for the actions to perform.

An improving field-oriented control technique without current sensors is proposed to control rotor speed for an induction motor drive. The estimated stator currents based on the slip frequency are used instead of feedback current signals in the field-oriented control (FOC) loop. The reference signals and the estimated currents through computation steps are used to generate the control voltage for the switching inverter. Simulations were performed in Matlab/Simulink environment at rated speed and low-speed range to demonstrate the method's feasibility. Through simulation results, the FOC method using virtual sensors has proved its effectiveness in ensuring the stable operation of the induction motor drive (IMD) over a wide speed range.

Sensor fusion algorithms are of fundamental importance for the odometric pose estimation of Differential Drive Wheeled Robots (DDWR) as it can be integrated into other algorithms to localize and map the environment. Different sensors are used for odometric pose estimation and, in some cases, Virtual Sensors (VS) are also used. The purpose of this work is to present a new VS can be integrated into sensor fusion algorithms for estimating the odometric pose of the robot. For this reason, a new VS for real-time estimation of the angular speed of the wheels connected to a DC of a DDWR for indoor applications is presented. The estimated wheel angular speed is obtained by processing the vibrational signals sampled from an onboard Inertial Measurement Unit (IMU) through the Discrete Fourier Transform (DFT). This technique is named Fast Fourier Transform as Wheel Angular Speed Estimator (FFT-WASE). The estimation process, integrated with the IMU, enables the realization of a new VS. The estimated angular speed obtained through the VS-based approach is adopted for making the speed control of the DDWR, as an application to validate the proposed technique. The comparison between the estimated and the measured speed coming from the encoders, the study of the absolute percentage errors and RMSE, the reconstruction of DDWR linear speed and yaw rate, and the stability analysis of the controlled DDWR have proved the validity of VS. Further tests are provided to show the applicability of the same acquired vibrational signals for real-time wheel slip monitoring.

This contribution presents a novel wear dependent virtual flow rate sensor for single stage single lobe progressing cavity pumps. We study the wear-induced material loss of the pump components and the impact of this material loss on the volumetric efficiency. The results are combined with an established backflow model to implement a backflow calculation procedure that is adaptive to wear. We use a laboratory test setup with a highly abrasive fluid and operate a pump from new to worn condition to validate our approach. The obtained measurement data show that the presented virtual sensor is capable of calculating the flow rate of a pump being subject to wear during its regular operation.

Variation in powertrain parameters caused by dimensioning, manufacturing and assembly inaccuracies may prevent model-based virtual sensors from representing physical powertrains accurately. Data-driven virtual sensors employing machine learning models offer a solution for including variations in the powertrain parameters. These variations can be efficiently included in the training of the virtual sensor through simulation. The trained model can then be theoretically applied to real systems via transfer learning, allowing a data-driven virtual sensor to be trained without the notoriously labour-intensive step of gathering data from a real powertrain. This research presents a training procedure for a data-driven virtual sensor. The virtual sensor was made for a powertrain consisting of multiple shafts, couplings and gears. The training procedure generalizes the virtual sensor for a single powertrain with variations corresponding to the aforementioned inaccuracies. The training procedure includes parameter randomization and random excitation. That is, the data-driven virtual sensor was trained using data from multiple different powertrain instances, representing roughly the same powertrain. The virtual sensor trained using multiple instances of a simulated powertrain was accurate at estimating rotating speeds and torque of the loaded shaft of multiple simulated test powertrains. The estimates were computed from the rotating speeds and torque at the motor shaft of the powertrain. This research gives excellent grounds for further studies towards simulation-to-reality transfer learning, in which a virtual sensor is trained with simulated data and then applied to a real system.

In this work, tailpipe carbon monoxide emission from a gasoline powertrain case study vehicle was analyzed for off-cycle (i.e., on road) driving to develop a virtual sensor. The vehicle was equipped with a portable emissions measurement system (PEMS) that measured carbon monoxide concentration and exhaust volumetric flowrate to calculate the mass of carbon monoxide emitted from the tailpipe. The vehicle was also equipped with a tailpipe electrochemical NOx sensor, and a correlation between its linear oxygen signal and the PEMS-measured carbon monoxide concentration was observed. The NOx sensor linear oxygen signal depends on the concentration of several reducing species, and a machine learning model was trained using this data and other features to target the PEMS-measured carbon monoxide mass emission. The model demonstrated a mean absolute percentage error (MAPE) of 19% when using 15 training drive cycles. Finally, a virtual carbon monoxide sensor was developed by removing the tailpipe NOx sensor information from the model feature set and predicting tailpipe carbon monoxide mass. The virtual model MAPE was shown to increase by 5% compared to the earlier version with a tailpipe NOx sensor over the same number of training, validation, and test drive cycles. The minimal degradation in accuracy for the virtual model was hypothesized to result from the fact that narrowband oxygen sensors may contain information regarding how rich or lean the exhaust gas is compared to stoichiometric conditions. This is analogous to the information provided by a wide-band oxygen sensor, but potentially with reduced resolution and accuracy. The data-driven approach was able to produce a novel virtual tailpipe carbon monoxide sensor in a gasoline powertrain using only common powertrain and emission sensors.

This research presents a data-driven Neural Network (NN)-based Virtual Sensor (VS) that estimates vehicles’ Unsprung Mass (UM) vertical velocity in real-time. UM vertical velocity is an input parameter used to control a vehicle’s semi-active suspension. The extensive simulation-based dataset covering 95 scenarios was created and used to obtain training, validation and testing data for Deep Neural Network (DNN). The simulations have been performed with an experimentally validated full vehicle model using software for advanced vehicle dynamics simulation. VS was developed and tested, taking into account the Root Mean Square (RMS) of Sprung Mass (SM) acceleration as a comfort metric. The RMS was calculated for two cases: using actual UM velocity and estimations from the VS as input to the suspension controller. The comparison shows that RMS change is less than the difference threshold that vehicle occupants could perceive. The achieved result indicates the great potential of using the proposed VS in place of the physical sensor in vehicles.

The design of chemical sensors and probes is usually based on selective receptors for individual analytes, however, many analytical tasks are dedicated to multi-analyte sensing or recognizing properties of the sample related to more than one analyte. While it is possible to simultaneously use multiple sensors/receptors in such cases, multi-responsive probes could be an attractive alternative. In this work, we use thiomalic acid-capped CdTe quantum dots as a multiple-response receptor for the detection and quantification of six heavy metal cations: Ag(I), Cd(II), Co(II), Cu(II), Ni(II), and Pb(II) at micromolar concentration levels. Multiplexing is realized via multispectral fluorescence (so-called virtual sensor array). For such a sensing strategy, the effective decoding of the excitation–emission spectrum is essential. Herein, we show how various parameters of chemometric analysis by the Partial Least Squares method, such as preprocessing type and data structure, influence the performance of discrimination and quantification of the heavy metals. The established models are characterized by respective performance metrics (accuracy, sensitivity, precision, specificity/RMSE, a, b, R2) determined for both train and test sets in replicates, to obtain reliable and repeatable results.

Accurate detection of gas/odor requires highly selective gas sensor. However, the high-performance classification of gases/odors can be achieved using partial-selective gas sensors. Since 1980s, an array of broadly tuned (partial-selective) gas sensors have been used in several fields of science and engineering, and the resulting gas sensing systems (GSS) are popularly known as electronic noses (e-Noses). The combination of similar or different sensors in the array indirectly compensates for the requirement of high selectivity in GSS. Further, e-Nose’s performance inevitably depends on the salient features drawn from the initial responses of the gas sensor array (GSA). So obtained features are referred to as the responses of virtual sensors (VS). In this paper, we have proposed the three-input and three-output (TITO) technique to derive efficient virtual sensor responses (VSRs) which outperform its well-published peer technique. A GSA consisting of four elements is used to demonstrate the proposed technique. Our proposed technique augments the VSRs by four times compared to its peer. The efficacy of our proposed technique has been tested using nine fundamental classifiers, viz., linear support vector machine (100%), decision tree (97.5%), multi-layer perceptron neural network (100%), K-nearest neighbor (85%), logistic regression (100%), Gaussian process with radial basis function (95%), linear discriminant analysis (97.5%), random forest (100%), and AdaBoost (95%). Ten-fold cross-validation has been used to minimize the biasing impact of the intra- and inter-class variance. With the result, four classifiers successfully provide an accuracy of 100 percent. Hence, we have proposed and vindicated an efficient technique.