Signal expected range for a vehicle

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.

A method for indicating failure of a vehicle component of a vehicle, the method comprising:

• retrieving a plurality of input signals that are correlated with a vehicle signal;
• determining a predicted vehicle signal from the plurality of input signals using a trained artificial intelligence model;
• 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 a 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 for occurrence of the anomalies, and
• indicating the failure of a vehicle component depending on the number of anomalies over a period of time.