Apparatus and method for processing sensor data to predict future outcomes
A method, apparatus, and system are described. The method includes generating a set of current values associated with at least one component included on a moving vehicle and providing the set of current values over a wireless network. The values are generated by one or more sensors. An edge computing device receives the current values. The method further includes processing the set of current values in real-time using at least one machine learning algorithm to identify a value of a point in time for a failure of one of the at least one component based on the set of current values and at least one set of past values received. The past values are stored in a memory. The set of current values are transmitted with a low time latency between the generating the set of current values and the processing of the set of current values.
1 . A method comprising:
generating a set of current values associated with at least one component included on a moving vehicle;
providing the set of current values over a wireless network;
processing the set of current values in real-time using at least one machine learning algorithm to identify a value of a point in time for a failure of one of the at least one component based on the set of current values and at least one set of past values received, the at least one machine learning algorithm being a dynamic linear regression algorithm utilizing at least one windowing operation, the at least one windowing operation performing a first windowing operation that includes an averaging of the set of current values and the at least one set of past values to produce a current set of first windowed values for the one of the at least one component, and wherein the at least one windowing operation further includes performing a second windowing operation on the current set of first windowed values, along with past sets of first windowed values, to produce a current set of second windowed values that are used to produce results at different points in time by the dynamic linear regression algorithm; and
providing, for display to a user, the value of the identified point in time for the failure the at least one component, the value of the identified point in time for the failure displayed in a different color when it is determined that the identified point in time for the failure is less than a threshold value for the point in time for the failure,
wherein the providing of the set of current values has a low time latency between the generating the set of current values and the processing of the set of current values.
2 . The method of claim 1 , wherein the identified value of the point in time for the failure is included as part of a graphical user interface for displaying operational elements of the moving vehicle.
3 . The method of claim 1 , wherein each one of the results at different points in time is compared to a threshold value to determine the point in time for a failure.
4 . The method of claim 1 , wherein the at least one component includes a tire and wherein the set of current values are generated by at least one load sensor coupled to the tire.
5 . The method of claim 1 , wherein the set of sensors generate a set of current values on a periodic basis.
6 . The method of claim 1 , wherein the wireless network is a 5G cellular network.
7 . The method of claim 1 , wherein the moving vehicle is a race vehicle.
8 . The method of claim 1 , wherein the low time latency is less than 100 milliseconds.
9 . A system comprising:
a set of sensors, each one of the set of sensors generating a set of current values associated with at least one component included on a moving vehicle, the generated set of current values being transmitted over a wireless network; and
an edge computing device that receives the set of current values transmitted over the wireless network, the edge computing device processing the set of current values in real-time using at least one machine learning algorithm to identify a value of a point in time for a failure of one of the at least one component a based on the set of current values and at least one set of past values received, the at least one machine learning algorithm being a dynamic linear regression algorithm utilizing at least one windowing operation, the at least one windowing operation performing a first windowing operation that includes an averaging of the set of current values and the at least one set of past values to produce a current set of first windowed values for the one of the at least one component, and wherein the at least one windowing operation further includes performing a second windowing operation on the current set of first windowed values, along with past sets of first windowed values, to produce a current set of second windowed values that are used to produce results at different points in time by the dynamic linear regression algorithm, the edge computing device further providing, for display to a user, the value of the identified point in time for the failure the at least one component, the value of the identified point in time for the failure displayed in a different color when it is determined that the identified point in time for the failure is less than a threshold value for the point in time for the failure;
wherein the generated set of current values are transmitted with a low time latency between the generating of the set of current values and the processing of the set of current values.
10 . An apparatus comprising:
a network interface that receives a set of current values over a wireless network, the set of current values generated from a set of sensors associated with at least one component included on a moving vehicle;
at least one processing device coupled to the network interface, the at least one processing device processing the set of current values in real-time using at least one machine learning algorithm to identify a value for a value of a point in time for a failure of one of the at least one component a based on the set of current values and at least one set of past values, the at least one machine learning algorithm being a dynamic linear regression algorithm utilizing at least one windowing operation, the at least one windowing operation performing a first windowing operation that includes an averaging of the set of current values and the at least one set of past values to produce a current set of first windowed values for the one of the at least one component, and wherein the at least one windowing operation further includes performing a second windowing operation on the current set of first windowed values, along with past sets of first windowed values, to produce a current set of second windowed values that are used to produce results at different points in time by the dynamic linear regression algorithm;
a memory coupled to the at least one processing device, the memory configured to store the set of current values and the at least one set of past values; and
an output interface coupled to the at least one processing device, the output device providing, for display to a user, the value of the identified point in time for the failure the at least one component, the value of the identified point in time for the failure displayed in a different color when it is determined that the identified point in time for the failure is less than a threshold value for the point in time for the failure,
wherein the current set of data values are received over the wireless network with a low time latency between the set of current values being generated and the set of current values being processed.
11 . The apparatus of claim 10 , wherein the at least one component includes a tire and wherein the set of current values are generated by at least one load sensor coupled to the tire.