Method and apparatus for diagnosing a failure

Patent metadata

Field	Value
Publication number	US-2025036509-A1
Application number	US-202318537963-A
Country	US
Kind code	A1
Filing date	Dec 13, 2023
Priority date	Jul 24, 2023
Publication date	Jan 30, 2025
Grant date	—

Abstract

A method for diagnosing a failure may include collecting sensor signals related to a vehicle in a form of time series data; obtaining a feature classified into a predetermined number of frequency indices by performing a Fourier transform on a predetermined time interval among the time series data; scaling the feature to a value of a predetermined range; generating a graph showing the value depending on a frequency index; generating a heatmap from the graph; training a normal trend figure model that represents trend of data indicating a normal state in the heatmap as a figure; and determining whether there is a failure by calculating a loss for a sensor data input based on the normal trend figure model.

First claim

What is claimed is:

1 . A method for diagnosing a failure, comprising:
collecting sensor signals related to a vehicle in a form of time series data; obtaining a feature classified into a predetermined number of frequency indices by performing a Fourier transform on a predetermined time interval among the time series data; scaling the feature to a value of a predetermined range; generating a graph showing the value based on a frequency index; generating a heatmap from the graph; training a normal trend figure model that represents trend of data indicating a normal state in the heatmap as a figure; and determining whether there is a failure by calculating a loss for a sensor data input based on the normal trend figure model.

2 . The method of claim 1 , wherein the normal trend figure model is represented as a figure having a shape having a uniform thickness and extending along an axis corresponding to the frequency index.

3 . The method of claim 2 , wherein the figure comprises a central line extending along the axis corresponding to the frequency index and comprises a peripheral region formed around the central line to have a predetermined width and to extend along the central line.

4 . The method of claim 2 , wherein:
a first section and a second section are set along the axis corresponding to the frequency index; the normal trend figure model is represented as a first figure of a shape having a first thickness in the first section and extending along the axis corresponding to the frequency index; and the normal trend figure model is represented as a second figure of a shape having a second thickness different from the first thickness in the second section and extending along the axis corresponding to the frequency index.

5 . The method of claim 1 , wherein the sensor signals comprise sensor signals related to at least one of a wheel speed, a longitudinal acceleration, a lateral acceleration, a vertical direction acceleration, a yaw rate, a roll rate, a pitch rate, a steering wheel angle, a steering wheel angular speed, or a vehicle sound.

6 . The method of claim 5 , wherein collecting the sensor signals comprises collecting the sensor signals through a controller area network (CAN),
wherein the CAN comprises at least one of a chassis CAN (C-CAN), a gateway CAN (G-CAN), or a powertrain CAN (P-CAN).

7 . The method of claim 5 , wherein collecting the sensor signals comprises collecting sound generated in the vehicle through a recording device.

8 . The method of claim 1 , wherein scaling comprises converting the feature to have a minimum value of 0 and a maximum value of 1 through minimum-maximum (Min-Max) normalization.

9 . The method of claim 1 , wherein the time series data comprises only normal driving data, and the normal trend figure model is trained based on the normal driving data.

10 . The method of claim 1 , wherein the loss is calculated by a loss function expressed as an equation of

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wherein, y i is an output value, ŷ i is a prediction value, and α i is a predetermined alpha value.

11 . The method of claim 10 , wherein the heatmap is converted into a graph having a binary color according to a predetermined ratio, and the predetermined alpha value is determined based on the graph.

12 . The method of claim 10 , wherein:
a first section and a second section are set along an axis corresponding to the frequency index; and the predetermined alpha value comprises a first alpha value determined corresponding to the first section and comprises a second alpha value determined corresponding to the second section and different from the first alpha value.

13 . A non-transitory computer-readable medium having a program or instructions recorded thereon, the program or instructions to direct a processor to perform acts of:
collecting sensor signals related to a vehicle in a form of time series data; obtaining a feature classified into a predetermined number of frequency indices by performing a Fourier transform on a predetermined time interval among the time series data; scaling the feature to a value of a predetermined range; generating a graph showing the value based on a frequency index; generating a heatmap from the graph; training a normal trend figure model that represents trend of data indicating a normal state in the heatmap as a figure; and determining whether there is a failure by calculating a loss for a sensor data input based on the normal trend figure model.

14 . An apparatus for diagnosing a failure, the apparatus, comprising:
a sensor signal collection module configured to collect sensor signals related to a vehicle in a form of time series data; a feature acquisition module configured to obtain a feature classified into a predetermined number of frequency indices by performing a Fourier transform on a predetermined time interval among the time series data; a scaling module configured to scale the feature to a value of a predetermined range; a heatmap generation module configured to generate a graph showing the value depending on a frequency index and generate a heatmap from the graph; a learning module configured to train a normal trend figure

Assignees

• Hyundai Motor Co Ltd
• Kia Corp

Inventors

• We Kyoungsoo
• Koo Jungmo
• Lee Hye Yeon

Classifications

• G06T11/26

  Drawing of charts or graphs · CPC title

• G06T11/23

  using straight lines or curves · CPC title

• G06F2218/12

  Classification; Matching · CPC title

• G06F2218/08

  Feature extraction · CPC title

• G06F2123/02

  in the time domain, e.g. time-series data · CPC title