US 12,394,260 B2
Signal expected range for a vehicle
Adrien Cossa, Stuttgart (DE); Sebastian Gropper, Donzdorf (DE); Klaus Merkle, Oberriexingen (DE); Victor Lemmel, Sersheim (DE); Andres Murube Lindahl, Stuttgart (DE); Matheus Duempelmann, Stuttgart (DE); Timo Basile, Forst (DE); Tushar Parulekar, Farmington Hills, MI (US); and Sanjiv Lancy, Farmington Hills, MI (US)
Assigned to Robert Bosch GmbH, Stuttgart (DE)
Filed by Robert Bosch GmbH, Stuttgart (DE)
Filed on Oct. 3, 2023, as Appl. No. 18/480,168.
Prior Publication US 2025/0111715 A1, Apr. 3, 2025
Int. Cl. G07C 5/08 (2006.01); B60L 58/30 (2019.01); F02D 41/22 (2006.01); F02M 26/49 (2016.01); G07C 5/10 (2006.01)
CPC G07C 5/0816 (2013.01) [B60L 58/30 (2019.02); F02D 41/22 (2013.01); F02M 26/49 (2016.02); G07C 5/0808 (2013.01); G07C 5/085 (2013.01); G07C 5/10 (2013.01); F02D 2200/501 (2013.01); F02D 2200/703 (2013.01)] 20 Claims
OG exemplary drawing
 
9. A method for indicating failure of a vehicle component by determining anomalies for a measured vehicle signal of a vehicle, the method comprising:
executing a trained artificial intelligence model with an electronic processor by:
retrieving a plurality of input signals that are correlated with the measured vehicle signal;
determining a predicted vehicle signal from the plurality of input signals;
determining a tolerance band from the plurality of input signals;
determining whether the model is valid,
when the model is valid generating a model valid signal;
retrieving the measured vehicle signal;
determining whether the measured vehicle signal is an anomaly based on the measured vehicle signal, the predicted vehicle signal, the tolerance band, and the model valid signal;
counting a number of and storing times and occurrences of the anomalies; and
indicating the failure of the vehicle component depending on the number of anomalies over a period of time.