Machine-learning based vehicle diagnostics and maintenance

A method of performing diagnostics, the method comprising:

• obtaining, from a plurality of sensors, a plurality of sensor signals representative of one or more states of a vehicle, wherein the plurality of sensors are not directly coupled to a wheel of the vehicle and none of the plurality of sensor signals defines a tire pressure of the wheel of the vehicle;
• providing an indirect tire pressure monitoring system (iTPMS) to indirectly identify a low tire pressure state based on the plurality of sensor signals;
• and
• applying a trained classifier to the plurality of sensor signals to identify one or more components of the vehicle that result in a change of vibration during operation of the vehicle and whether a cause of the vibrations during the operation of the vehicle is the low tire pressure state of the wheel of the vehicle, wherein the trained classifier includes both a trained random forest classifier trained using supervised learning and a trained hidden Markov model trained using unsupervised learning.

A device configured to perform diagnostics, the device comprising:

• a hardware interface configured to communicate with a plurality of sensors to obtain a plurality of sensor signals representative of one or more states of a vehicle, wherein the plurality of sensors are not directly coupled to a wheel of the vehicle and none of the plurality of sensor signals defines a tire pressure of the wheel of the vehicle;
• and
• one or more processors configured to:
• provide an indirect tire pressure monitoring system (iTPMS) to indirectly identify a low tire pressure state based on the plurality of sensor signals;
• and
• apply a trained classifier with respect to the plurality of sensor signals to identify one or more components of the vehicle that result in a change of vibration during operation of the vehicle and whether a cause of the vibrations during the operation of the vehicle is the low tire pressure state of the wheel of the vehicle, wherein the trained classifier includes both a trained random forest classifier trained using supervised learning and a trained hidden Markov model trained using unsupervised learning.

A non-transitory computer-readable medium having stored thereon instructions that, when executed, cause one or more processors of a diagnostic device to:

• interface with a plurality of sensors to obtain a plurality of sensor signals representative of one or more states of a vehicle, wherein the plurality of sensors are not directly coupled to a wheel of the vehicle and none of the plurality of sensor signals defines a tire pressure of the wheel of the vehicle;
• provide an indirect tire pressure monitoring system (iTPMS) to indirectly identify a low tire pressure state based on the plurality of sensor signals;
• and
• apply a trained classifier with respect to the plurality of sensor signals to identify one or more components that result in a change of vibration during operation of the vehicle and whether a cause of the vibrations during the operation of the vehicle is the low tire pressure state of the wheel of the vehicle, wherein the trained classifier includes both a trained random forest classifier trained using supervised learning and a trained hidden Markov model trained using unsupervised learning.