APPARATUS AND METHOD FOR PREDICTING MIXED PROBLEMS WITH VEHICLE

• US 20120245791A1
• Filed: 10/28/2011
• Published: 09/27/2012
• Est. Priority Date: 03/22/2011
• Status: Abandoned Application

• Current Assignee: Chungbuk National University Industry Academic Cooperation Foundation (Chungbuk National University), Electronics and Telecommunications Research Institute
• Original Assignee: Chungbuk National University Industry Academic Cooperation Foundation (Chungbuk National University), Electronics and Telecommunications Research Institute
• Inventors: Lee, Jeong-Woo, Oh, Hyun-Seo, Yun, Un-Il, Lee, Shin-Kyung, Shin, Hyeon-Il, Pyun, Gwang-Bum, Kwon, Oh-Cheon, Ryang, Heung-Mo
• Application Number:
  US13/284,780
• Publication Number:
  US 20120245791A1
• Time in Patent Office: Days
• US Class Current: 701/31.9
• CPC Class Codes: B60W 50/0205 Diagnosing or detecting failures; Failure detection models
• CPC Class Codes: F02D 41/1405 Neural network control

Abstract

The apparatus includes a data normalization unit, a neural network problem prediction unit, and a transition change prediction unit. The data normalization unit creates normalization transformation values by performing normalization transformation based on threshold value ranges for a plurality of pieces of vehicle network data. The neural network problem prediction unit creates a neural network problem prediction value by predicting a mixed problem with the vehicle using a multi-artificial neural network model, created based on a learning data set related to mixed problems having previously occurred in the vehicle and the normalization transformation values. The transition change prediction unit predicts a change in transition for the mixed problem according to a change in the neural network problem prediction value, by analyzing the neural network problem prediction value and previous neural network problem prediction values previously created in the vehicle.

Claims

1. An apparatus for predicting mixed problems with a vehicle, comprising:

a data normalization unit for creating normalization transformation values by performing normalization transformation based on threshold value ranges for a plurality of pieces of vehicle network data transferred by the vehicle;

a neural network problem prediction unit for creating a neural network problem prediction value by predicting a mixed problem with the vehicle using a multi-artificial neural network model, created based on a learning data set related to mixed problems having previously occurred in the vehicle, and the normalization transformation values; and

a transition change prediction unit for predicting a change in transition for the mixed problem according to a change in the neural network problem prediction value, by analyzing the neural network problem prediction value and previous neural network problem prediction values previously created in the vehicle.

2. The apparatus as set forth in claim 1, further comprising a prediction result analysis unit for determining whether to immediately provide notification of the mixed problem or to predict the change in transition depending on results of comparison between the neural network problem prediction value and a reference problem value range.

3. The apparatus as set forth in claim 2, wherein the prediction result analysis unit:

immediately provides notification of the mixed problem when the neural network problem prediction value exceeds the reference problem value range; and

transfers the neural network problem prediction value to the transition change prediction unit when the neural network problem prediction value includes within the reference problem value range.

4. The apparatus as set forth in claim 2, wherein:

the multi-artificial neural network model comprises an input layer, a hidden layer, and an output layer; and

the neural network problem prediction unit sets an input weight of artificial neural network nodes between the input layer and the hidden layer, and creates the multi-artificial neural network model by learning the hidden layer based on the learning data set

5. The apparatus as set forth in claim 4, wherein the hidden layer creates the neural network problem prediction value in accordance with a relationship between the normalization transformation values based on the learning data set.

6. The apparatus as set forth in claim 4, wherein:

the threshold value ranges is set to values between a minimum threshold value and a maximum threshold value; and

the data normalization unit performs normalization transformation of a vehicle network data into a first value when the vehicle network data is the minimum threshold value or the maximum threshold value, and performs normalization transformation of the vehicle network data into a second value different from the first value when the vehicle network data is a mid-value between the minimum and maximum threshold values.

7. The apparatus as set forth in claim 6, wherein the data normalization unit performs normalization transformation into a third value larger than the second value and smaller than the first value when the vehicle network data is larger than the minimum threshold value and smaller than the mid-value or when the vehicle network data is larger than the mid-value and smaller than the maximum threshold value.

8. A method of predicting mixed problems with a vehicle, comprising.

creating a multi-artificial neural network model based on a learning data set related to mixed problems having previously occurred in the vehicle;

creating normalization transformation values based on threshold value ranges for a plurality of pieces of vehicle network data transferred by the vehicle;

creating a neural network problem prediction value by predicting a mixed problem with the vehicle using the multi-artificial neural network model and the normalization transformation values; and

determining whether to immediately provide notification of the mixed problem or to predict the change in transition change depending on results of comparison between the neural network problem prediction value and a reference problem value range.

9. The method as set forth in claim 8, wherein the creating a multi-artificial neural network model comprises:

setting an input weight of artificial neural network nodes between an input layer and a hidden layer included the multi-artificial neural network; and

creating the multi-artificial neural network model by learning the hidden layer based on the learning data set.

10. The method as set forth in claim 9, wherein the creating a neural network problem prediction value comprises:

applying the input weight of the artificial neural network nodes to the normalization transformation values transferred to the input layer, and transferring a resulting value to the hidden layer; and

creating the neural network problem prediction value in accordance with a relationship between the normalization transformation values based on the learning data set.

11. The method as set forth in claim 10, wherein the creating a normalization transformation values comprises:

performing normalization transformation of a vehicle network data into a first value when the vehicle network data is a minimum or maximum threshold value of the threshold value ranges; and

performing normalization transformation of the vehicle network data into a second value different from the first value when the vehicle network data is a mid-value between the minimum and maximum threshold values.

12. The method as set forth in claim 11, wherein the creating a normalization transformation values comprises performing normalization transformation into a third value larger than the second value and smaller than the first value when the vehicle network data is larger than the minimum threshold value and smaller than the mid-value or when the vehicle network data is larger than the mid-value and smaller than the maximum threshold value.

13. The method as set forth in claim 11, wherein the determining whether to predict the change in transition change comprises:

immediately providing notification of the mixed problem when the neural network problem prediction value exceeds the reference problem values; and

predicting a change in transition for the mixed problem according to a change in the neural network problem prediction value, when the neural network problem prediction value includes within the reference problem value range.