The running stability of a railway vehicle, which is highly relevant for operational safety, strongly depends on the shapes of the profiles of wheel and rail, which change during the progress of wear. The model-based condition monitoring of a key parameter, such as the wheelsets equivalent conicity, is one of the approaches to realize condition-based maintenance and ensure railway vehicles reliability and running safety. A constrained nonlinear estimation technique is developed in this paper concerning the monitoring of the equivalent conicity. Suitable modelling of the railway vehicle dynamics is presented for the model-based estimator synthesis to perform the equivalent conicity monitoring employing only the carbody and bogie frame measurements, taking into account the track adhesion level variation. This approach allows avoiding the wheelset sensorization affected by a harsh and vibrational environment, thus reducing the possibility to produce inaccurate measurements. Another direct consequence of this approach is the reduction of costs relative to vehicle sensorization. Moreover, the possibility to constraint the estimated vehicle states and parameters allows considering their operative acceptance limits.

The employment of intelligent tires to test the vehicle performances is ever-increasing in the last years. Many research activities have been made to correlate measurements provided by sensors to the tire dynamics. In this paper, a novel tire-road forces estimation technique is presented. The developed estimator is based on an approximation of the well-known Flexible Ring Tire Model (FRTM) and therefore, it is suitable for real-time normal and tangential forces identification. Only the tread band strain measurements are employed in the developed algorithm which can estimate the tire-road forces at every wheel revolution. The tool is validated through both numerical and experimental tests. The results indicate that the developed estimation algorithm, obtained by a mathematical inversion of the FRTM, can be employed as a monitoring tool for tires and vehicles.

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.