Patent Application
20060155734
The present invention relates generally to quality control, and more particularly to evaluating vehicles and other dynamic systems.
When cars, trucks and other vehicles are manufactured, testing typically is performed on various systems of test vehicles to confirm whether the vehicles meet applicable design specifications and are operating as intended. Many vehicle systems, however, are dynamic; that is, they change in response to various inputs. It can take time for such a system to respond to an input, and it can be difficult to capture such inputs and responses in a meaningful way in a testing procedure.
The present invention, in one embodiment, is directed to 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).
In another implementation, a method of evaluating whether a response over time of a vehicle under test is within an expected range includes sampling parameters of the vehicle to obtain a plurality of sets of data samples. A first of the sample sets is input to an artificial neural network (ANN). A data sample from the first sample set is included in a second of the sample sets. The second sample set is input to the ANN.
In another configuration, an evaluating apparatus for evaluating responses over time by a subject vehicle includes a sampling apparatus that obtains a plurality of data samples from the vehicle. A processor inputs the data samples as a plurality of sample sets to a self-organizing map (SOM). The processor includes one of the data samples in more than one of the sample sets.
In yet another configuration, the invention is directed to an evaluating apparatus for evaluating one or more time-varying parameters in a system under test. A sampling apparatus obtains from the system a plurality of data samples describing the parameters at a plurality of sample times. A processor includes a time series of the data samples in a sample set, and inputs the sample set to a self-organizing map (SOM).
Further areas of applicability of the present invention will become apparent from the detailed description provided hereinafter. It should be understood that the detailed description and specific examples, while indicating exemplary embodiments of the invention; are intended for purposes of illustration only and are not intended to limit the scope of the invention.
The present invention will become more fully understood from the detailed description and the accompanying drawings, wherein:
The following description of various embodiments of the present invention is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses. The present invention, in one implementation, is directed to using an artificial neural network (ANN) to provide metrics relevant to a dynamic system, i.e., a system that changes over time. In a dynamic system, it can take time for a parameter of the system to respond to an input to the system.
When the ANN is implemented in accordance with one embodiment of the present invention, a relationship may be detected between a system input and a system output that occurs later in time. Although implementations of the present invention are described in connection with a two-dimensional self-organizing map (SOM), the invention is not so limited. Implementations also are contemplated in connection with other types of SOMs and other types of ANNs. Additionally, although embodiments of the invention are described in connection with evaluating vehicle systems, the invention may be practiced in connection with various dynamic and/or static systems, including but not limited to vehicle systems.
An embodiment of an evaluation apparatus is indicated generally in
Generally, in an ANN, processing elements (“neurons”) are connected to other neurons of the ANN with varying strengths of connection. As the connections are adjusted, the ANN “learns” to output results appropriate to the task at hand. The self-organizing map (SOM) 70 is a type of ANN that is useful in performing quality control. The SOM 70 can be used, for example, to identify what is a “normal” result of a manufacturing process. A “normal” result means, for example, that all manufactured parts are within specification and operating as designed. In the present configuration, the SOM 70 is trained to “remember” data between one sample set and another sample set, as further described below.
The SOM 70 is shown in greater detail in
Before being used to evaluate the system 28, the SOM 70 is trained in the following manner. A plurality of sample sets are input to the SOM 70. A sample set may be, e.g., a vector of data values collected from sampling points relative, for example, to a motor and/or other component(s) of the vehicle 42 as previously described with reference to
The foregoing process of sampling and inputting sample sets to the SOM 70 is repeated for a number of sample sets appropriate to train the SOM 70 to recognize, for example, “normal” interrelationships among data values taken from “normal” vehicles. Eventually the neurons 128 tend to “self-organize” by re-weighting neighborhood relations 134, such that distances between the neurons 128 are reduced.
After having been trained in the foregoing manner, the SOM 70 may be used to evaluate a system. The SOM may be exposed, for example, to data taken from subject vehicles under test, e.g., data taken from the system 28 of the vehicle 42. For each sample set taken from subject vehicles, the SOM may locate a neuron that best matches the data in the sample set. The SOM also can indicate how close the data is to the closest neuron. By aggregating such SOM results, one can provide a metric to indicate whether a vehicle under test is operating as intended. Thus a vehicle that operates outside design expectations can be identified.
The system 28 is sampled to obtain a plurality of sets of data samples, as previously described with reference to
Exemplary sample sets of data in accordance with one implementation of the present invention are indicated generally in
Accordingly, the sample set 212 includes, at the location 222, a data sample dn+1 taken by the sampling apparatus 50 from the system 28 at a sample time n+1 following the sample time n. In the same manner, the locations 226 and 232 of the sample set 212 include data samples dn and dn−m+1 respectively, taken at sample times n and n−m+1.
Thus the processor 60 includes data samples from more than one of the sample times in a sample set, which is input to the SOM 70. The SOM 70 can be provided with a time series of data in 0 each sample set. The SOM 70 thereby can be trained to evaluate relationships, for example, between an input to the system at time n−m and an output of the system at time n. Expressed differently, a sample set n is input to the SOM 70. At least a portion of data from the sample set n is included in a sample set n+1 which is input to the SOM.
An example of using a SOM in accordance with one implementation of the present invention shall now be described. Nine motors were simulated in a test as further described below. Five of the motors (specifically, TestMotor_1 through TestMotor_5) were pre-designated as being within specification (i.e., “normal”). The other four motors (specifically, TestMotor_BackEMF_Var, TestMotor_Friction_Var, TestMotor_InertiaResistance_Var, and TestMotor_Resistance_Var) included parameters that were pre-set to values outside a “normal” distribution. For example, TestMotor_BackEMF_Var had back EMF gain pre-set outside the “normal” distribution.
A SOM processed input representing 1,000 sample times, each sample time separated from a previous and/or a subsequent sample time by one second. Sample data values input to the SOM for each motor and for each sample time included an input reference voltage Vc (ref). Sample data values input to the SOM also included such motor outputs as the last five samples of voltage, the last five samples of current, and the last five samples of motor speed.
A graph of data relating to the above described simulation is indicated generally in
Many different metrics are possible using various implementations of the present invention. For example, a chart indicated generally in
Embodiments of the foregoing apparatus and methods allow a SOM to be utilized with respect to a dynamic system such as a car or truck to identify variation in mass production. ANNs can be used to evaluate several parameters at once and thus are capable of detecting relatively subtle variations or combinations of parameters that might not be detected by single-parameter comparisons. SOMs can learn what is “normal” or expected and then compare data from mass-produced vehicles to more easily discover non-obvious variation in vehicle parameters.
The foregoing methods and apparatus can be applied at vehicle pilot production to determine whether pilot vehicles perform the same as development vehicles. Embodiments also can be used at end-of-line testing to identify variations in a manufacturing process. Data gathered from vehicles in the field could be compared to data collected from dealers or from telematic data collection systems. Many time-varying parameters, including but not limited to various response times, could be detected and evaluated. Additionally, information gained from evaluating such parameters could be useful in detecting environmental and/or application-varying parameters such as temperature, humidity, and/or parameters connected with vehicle operation in mountainous areas.
Those skilled in the art can now appreciate from the foregoing description that the broad teachings of the present invention can be implemented in a variety of forms. Therefore, while this invention has been described in connection with particular examples thereof, the true scope of the invention should not be so limited since other modifications will become apparent to the skilled practitioner upon a study of the drawings, specification, and the following claims.
