Cardan Shaft Moment Estimation via Torsional Oscillator Chain

The patent introduces a Kalman filter as an intermediary computational layer that processes readily available sensor data (torque, angular velocity, braking moment) to estimate cardan shaft moments. This mediator transforms inexpensive, standard sensor inputs into precise moment estimates without requiring complex dedicated measurement hardware, thereby resolving the contradiction between measurement precision and device complexity

The patent replaces physical mechanical measurement devices (such as torque sensors on cardan shafts) with a computational/mathematical model approach. By using a state space model with Kalman filtering that processes electrical sensor signals, the system substitutes complex mechanical measurement systems with simpler computational algorithms, achieving precise moment detection while reducing device complexity and cost

The patent performs preliminary actions by pre-defining the state space model structure, state vectors, and system matrices during the design phase. The Kalman filter parameters and model coefficients are predetermined, allowing the system to execute only efficient parameter updates during real-time operation rather than performing full simulations, thus achieving both precision and real-time performance

The patent transforms the static off-line simulation approach into a dynamic real-time estimation system. By using a dynamic state space model with time-varying state vectors and recursive Kalman filter updates, the system adapts continuously to changing operating conditions while maintaining computational efficiency. The recursive nature of the Kalman filter allows real-time adaptation without re-running full simulations, resolving the contradiction between precision and speed