Abstract: Techniques for monitoring and predicting vehicle health are disclosed. In some examples, sensor data (e.g., audio data) may be used to create a sensor signature associated with a vehicle component. The sensor signature may be compared with one or more second sensor signatures associated with the vehicle component over the life of the vehicle component to determine changes in an operating status associated with the vehicle component. In some examples, a machine learned model may be trained to identify a vehicle component and/or and operating status of a vehicle component based on sensor data that is inputted into the machine learned model. In this way, sensor data may be input into the machine learned model and the machine learned model may output a corresponding vehicle component and/or operating status associated with the component.

Abstract: Techniques for monitoring and predicting vehicle health are disclosed. In some examples, sensor data (e.g., audio data) may be used to create a sensor signature associated with a vehicle component. The sensor signature may be compared with one or more second sensor signatures associated with the vehicle component over the life of the vehicle component to determine changes in an operating status associated with the vehicle component. In some examples, a machine learned model may be trained to identify a vehicle component and/or and operating status of a vehicle component based on sensor data that is inputted into the machine learned model. In this way, sensor data may be input into the machine learned model and the machine learned model may output a corresponding vehicle component and/or operating status associated with the component.

Abstract: Techniques for monitoring and predicting vehicle health are disclosed. In some examples, sensor data (e.g., audio data) may be used to create a sensor signature associated with a vehicle component. The sensor signature may be compared with one or more second sensor signatures associated with the vehicle component over the life of the vehicle component to determine changes in an operating status associated with the vehicle component. In some examples, a machine learned model may be trained to identify a vehicle component and/or and operating status of a vehicle component based on sensor data that is inputted into the machine learned model. In this way, sensor data may be input into the machine learned model and the machine learned model may output a corresponding vehicle component and/or operating status associated with the component.

Abstract: Techniques for monitoring and predicting vehicle health are disclosed. In some examples, sensor data (e.g., audio data) may be used to create a sensor signature associated with a vehicle component. The sensor signature may be compared with one or more second sensor signatures associated with the vehicle component over the life of the vehicle component to determine changes in an operating status associated with the vehicle component. In some examples, a machine learned model may be trained to identify a vehicle component and/or and operating status of a vehicle component based on sensor data that is inputted into the machine learned model. In this way, sensor data may be input into the machine learned model and the machine learned model may output a corresponding vehicle component and/or operating status associated with the component.