Apparatus and methods for evaluating a dynamic system
View Patent ↗A method of evaluating whether a vehicle under test is operating as intended. Parameters of the vehicle are sampled at a plurality of sample times to obtain a plurality of data samples. Data samples from more than one of the sample times are included in a sample set. The sample set is input to an artificial neural network (ANN). Many time-varying parameters, e.g., response times in motor vehicle systems, can be detected and evaluated.
1. An evaluating apparatus for evaluating responses over time by a subject vehicle, said apparatus comprising:
a sampling apparatus configured to obtain a first plurality of data samples from the vehicle; and
a processor that includes a self-organizing map (SOM), the processor is configured to input the first plurality of data samples as a first plurality of sample sets to the SOM,
wherein said processor is configured to include one of the first plurality of data samples in more than one of the first plurality of sample sets, and
wherein said processor is configured to train the SOM to remember normal data from the first plurality of sample sets by recognizing normal interrelationships among the first plurality of data samples.
2. The evaluating apparatus of claim 1 wherein said processor includes the one of the first plurality of data samples in more than one of the first plurality of sample sets based on a sample time associated with the one of the first plurality of data samples.
3. The evaluating apparatus of claim 1 wherein one of the first plurality of sample sets comprises data samples obtained by said sampling apparatus at a plurality of sample times,
wherein a data sample of a first sample set is obtained at one of the plurality of sample times and is included in a second sample set, and
wherein the second sample set is obtained at another one of the plurality of sample times.
4. The evaluating apparatus of claim 1 wherein said processor is configured to evaluate relationships between data samples of a second plurality of samples sets based on the SOM.
5. The evaluating apparatus of claim 1 wherein the subject vehicle includes a motor and said sampling apparatus is configured to obtain the first plurality of data samples from sensors of the motor.
6. The evaluating apparatus of claim 1 wherein said processor is external from said vehicle.
7. The evaluating apparatus of claim 1 wherein said processor is remote from said vehicle.
8. The evaluating apparatus of claim 1 wherein said first plurality of sample sets are associated with different vehicles.
9. The evaluating apparatus of claim 1 wherein said first plurality of sample sets comprise:
a first sample set associated with training based on data from a first vehicle; and
a second sample set associated with testing of a second vehicle.
10. The evaluating apparatus of claim 1 wherein said processor is configured to identify variations in a manufacturing process based on said SOM.
11. The evaluating apparatus of claim 1 wherein said processor is configured to detect that said vehicle is operating outside design specifications based on the normal data from the first plurality of sample sets.
12. The evaluating apparatus of claim 1 wherein said processor is configured to determine that the vehicle is operating outside design specifications based on said SOM, and
wherein said SOM includes a data set collected during training with another vehicle.
13. The evaluating apparatus of claim 1 wherein said processor is configured to detect variations between data sets collected from different vehicles based on said SOM.
14. The evaluating apparatus of claim 1 wherein the processor is configured to generate and adjust neurons based on the first plurality of sample sets, and
wherein the processor is configured to update weights of relations between the neurons.
15. The evaluating apparatus of claim 14 wherein the processor is configured to self-organize the neurons by re-weighting the relations to reduce distances between the neurons.
16. The evaluating apparatus of claim 1 wherein the processor is configured to generate and locate a neuron that best matches the first plurality of sample sets based on the SOM.
17. The evaluating apparatus of claim 1 wherein the processor is configured to generate neurons based on the first plurality of sample sets, and
wherein the processor is configured to determine distances between the data samples of the first plurality of sample sets and a neuron based on the SOM.
18. The evaluating apparatus of claim 1 wherein the SOM comprises distances between the data samples of the first plurality of sample sets and neurons.
19. The evaluating apparatus of claim 1 wherein the processor is configured to generate neurons based on the first plurality of sample sets, and
wherein the processor is configured to determine which one of the neurons is closest to the first plurality of sample sets based on the SOM.
20. The evaluating apparatus of claim 1 wherein the first plurality of sample sets is associated with a first engine,
wherein the processor is configured to input a second plurality of data samples as a second plurality of sample sets associated with a second engine to the SOM,
wherein the first plurality of sample sets includes an input reference voltage, and
wherein the second plurality of sample sets includes the input reference voltage.
21. The evaluating apparatus of claim 1 wherein the processor is configured to input sample sets for a plurality of engines to the SOM, and
wherein the processor is configured to determine whether output data from an engine of the vehicle is normal based on the SOM.
22. The evaluating apparatus of claim 21 wherein the sampling apparatus is configured to sample the output data within a predetermined period after a sampling of a reference voltage, and
wherein each of the input sample sets includes the reference voltage.
23. The evaluating apparatus of claim 3 wherein the processor is configured to train the SOM to evaluate a relationship between an input of an engine of the vehicle at a first time and an output of the engine at a second time based on the data sample of the first sample set.