Method of automatically creating ai diagnostic model for diagnosing abnormal state based on noise and vibration data to which enas is applied

Patent metadata

Field	Value
Publication number	US-2023351174-A1
Application number	US-202217949441-A
Country	US
Kind code	A1
Filing date	Sep 21, 2022
Priority date	Apr 27, 2022
Publication date	Nov 2, 2023
Grant date	—

Abstract

A method of automatically creating an artificial intelligence (AI) diagnostic model for diagnosing an abnormal state of a vehicle includes: acquiring noise and vibration data measured by a sensor of the vehicle as input data, processing the input data, searching and selecting an architecture of the AI diagnostic model based on the processed input data, and providing the AI diagnostic model to diagnose the abnormal state of the vehicle, where an efficient neural architecture search (ENAS) is applied to update the AI diagnostic model and a parameter configuring the AI diagnostic model, the ENAS sharing the parameter with the updated AI diagnostic model.

First claim

What is claimed is:

1 . A method of automatically creating an artificial intelligence (AI) diagnostic model for diagnosing an abnormal state of a vehicle, the method comprising:
acquiring noise and vibration data measured by a sensor of the vehicle as input data; processing the input data; searching and selecting an architecture of the AI diagnostic model based on the processed input data; and providing the AI diagnostic model to diagnose the abnormal state of the vehicle, wherein an efficient neural architecture search (ENAS) is applied to update the AI diagnostic model and a parameter configuring the AI diagnostic model, the ENAS sharing the parameter with the updated AI diagnostic model.

2 . The method of claim 1 , wherein searching and selecting the architecture of the AI diagnostic model includes parameter tuning and controller training.

3 . The method of claim 2 , wherein the parameter tuning includes:
creating a sampled architecture string to transmit, to a proxy model, the created architecture string by a recurrent neural network (RNN) controller.

4 . The method of claim 3 , wherein the parameter tuning includes:
transmitting, to the proxy model, training data among the processed input data, the training data divided into a plurality of data.

5 . The method of claim 4 , wherein the parameter tuning includes:
updating the parameter in the proxy model.

6 . The method of claim 2 , wherein the controller training includes:
creating a sampled architecture string to transmit, to a proxy model, the created architecture string by a RNN controller.

7 . The method of claim 6 , wherein the controller training further includes:
transmitting, to the proxy model, validation data among the input data, the validation data divided into a plurality of data.

8 . The method of claim 7 , wherein the controller training further includes:
measuring accuracy in the proxy model with a different architecture of the AI diagnostic model.

9 . The method of claim 8 , wherein the controller training further includes:
updating a value of the parameter using a reinforced training that increases the measured accuracy by performing reinforcement leaning for a reward, and training the RNN controller by the updated value of the parameter.

10 . The method of claim 1 , wherein searching and selecting the architecture of the AI diagnostic model includes:
searching for the AI diagnostic model that searches for a unit model including a normal cell and a reduction cell.

11 . The method of claim 10 , further comprising:
validating the AI diagnostic model, wherein, based on (i) a level of accuracy of the AI diagnostic model being greater than a predefined level and (ii) a number of layers greater than or equal to a predefined number being added to the AI diagnostic model according to a change in a depth of the AI diagnostic model, a training process is terminated when a change rate of the accuracy converges to a level equal to or less than a predetermined level.

12 . The method of claim 11 ,
wherein the AI diagnostic model is (i) provided as an API in a server or (ii) stored in a file as a user device environment.

13 . A method of automatically creating an artificial intelligence (AI) diagnostic model for diagnosing an abnormal state of a vehicle, the method comprising:
acquiring noise and vibration data measured by a sensor of the vehicle as input data; processing the input data; extracting one or more features from the processed input data; selecting a combination of features suitable for the AI diagnostic model from the extracted one or more features; searching and selecting an architecture of the AI diagnostic model based on the processed input data; optimizing the architecture of the AI diagnostic model based on a parameter; validating the AI diagnostic model that is configured to, based on (i) accuracy of the AI diagnostic model being greater than a predefined level and (ii) a number of layers greater than or equal to a predefined number being added to the AI diagnostic model according to a change in a depth of the AI diagnostic model, terminate a training process when a change rate of the accuracy converges to a level equal to or less than a predetermined level; and providing the AI diagnostic model to diagnose the abnormal state of the vehicle, wherein an efficient neural architecture search (ENAS) is applied to update the AI diagnostic model and the parameter configuring the AI diagnostic model, the ENAS sharing the parameter with the updated AI diagnostic model.

14 . The method of claim 13 , wherein searching and selecting the architecture of the AI diagnostic model includes parameter tuning and controller training.

15 . The method of claim 14 , wherein the parameter tuning includes:
creating a sampled architecture string to transmit, to a proxy model, the created architecture string by a recurrent neural network (RNN) controller.

16 . The method of claim 15 , wherein the parameter tuning includes:
transmitting, to the proxy model, training data among the processed input data, the training data divided into a plurality of data.

17 . The method of claim 16 , wherein the parameter tuning includes:
updating the parameter in the proxy model.

18 . The method of claim 14 , wherein the controller training includes:
creating a sampled architecture string to transmit, to a proxy model, the created architecture string by a RNN controller.

19 . The method of claim 18 , wherein the controller training further includes:
transmitting, to the proxy model, validation data among the input data, the validation data divided into a plurality of data.

20 . The method of claim 13 ,
wherein the AI diagnostic model is (i) provided as an API in a server or (ii) stored in a file as a user device environment.

Assignees

• Hyundai Motor Co Ltd
• Kia Corp
• Iucf Hyu

Inventors

• Lee Dong-Chul
• Jung In-Soo
• Lee Joo-Hyun
• Chang Joon-Hyuk
• Noh Kyoung-Jin

Classifications

• G06Q50/10

  Services · CPC title

• G06N3/04 Primary

  Architecture, e.g. interconnection topology · CPC title

• G06N3/08 Primary

  Learning methods · CPC title

• G07C5/0808

  Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005) · CPC title

• G06N3/0464 Primary

  Convolutional networks [CNN, ConvNet] · CPC title