Training method for fault detection model, device fault detection method and related apparatus

Patent metadata

Field	Value
Publication number	US-2025191164-A1
Application number	US-202418956420-A
Country	US
Kind code	A1
Filing date	Nov 22, 2024
Priority date	Dec 12, 2023
Publication date	Jun 12, 2025
Grant date	—

Abstract

Provided is a training method for a fault detection model, a device fault detection method and related apparatuses, relating to the field of computer technology. The method comprises: sampling a designated device based on a preset drone formation to obtain a sample sequence; performing position encoding on the sample sequence according to the drone formation to obtain a drone formation encoding result; inputting the sample sequence and the drone formation encoding result into a model to be trained to obtain a fault detection result output by the model to be trained; determining a loss value based on the fault detection result and a true value of a fault detection result of the sample sequence; and adjusting a model parameter of the model to be trained based on the loss value to obtain the fault detection model.

First claim

What is claimed is:

1 . A training method for a fault detection model, comprising:
sampling a designated device based on a preset drone formation to obtain a sample sequence; performing position encoding on the sample sequence according to the drone formation to obtain a drone formation encoding result; inputting the sample sequence and the drone formation encoding result into a model to be trained to obtain a fault detection result output by the model to be trained; determining a loss value based on the fault detection result and a true value of a fault detection result of the sample sequence; and adjusting a model parameter of the model to be trained based on the loss value to obtain the fault detection model; wherein the model to be trained comprises an encoder and a decoder; the encoder is configured to perform feature extraction on each sample image in the sample sequence to obtain a multi-scale feature of each sample image; perform feature fusion on features in a same scale of the sample sequence based on the drone formation encoding result to obtain a fused feature corresponding to each scale; and perform feature fusion on fused features in all scales to obtain a target feature; and the decoder is configured to determine a fault detection result of the designated device based on the target feature, wherein the fault detection result comprises a fault prediction type and a fault prediction box of a same fault position in a fault sample map; and splice sample images in the sample sequence with reference to the drone formation to obtain the fault sample map.

2 . The method of claim 1 , further comprising: determining the true value of the fault detection result of the sample sequence by:
splicing the sample sequence into the fault sample map according to the drone formation, wherein the fault sample map describes a state of the designated device from a plurality of drone perspectives; constructing first prompt information based on the fault sample map, wherein the first prompt information comprises a fault point of at least one fault in the fault sample map, and position information of detection boxes of a same fault in different drone perspectives in the fault sample map is used as sub-position parameters; performing position encoding on the sub-position parameters of the same fault to obtain a fault position code of the same fault; and for each fault, performing following operations: inputting a fault point of the fault and a fault position code of the fault as second prompt information into an everything segmentation model, so that the everything segmentation model segments out a fault mask map of the fault from the fault sample map; and obtaining a true class label of the fault mask map of the fault, and constructing a detection box label of the fault based on position information of the fault mask map in the fault sample map, to obtain the true value of the fault detection result.

3 . The method of claim 1 , wherein a loss function of the model to be trained comprises following loss items:
position loss between the fault prediction box and detection box label; and classification loss between the fault prediction type and true class label.

4 . The method of claim 3 , wherein the classification loss is determined based on a following formula:

l

cls

=

-

1
N

⁢

∑
i
N

log
⁢

(

e

f
⁡
(

a
i

,

b
i

)

/

ρ
τ

∑

b
′

·

e

f
⁡
(

a
i

,

b
′

)

/

ρ
τ

)

wherein N is a quantity of fault positions in the fault sample map, and a same fault position is counted once in N when there are a plurality of sample images describing the same fault position in the fault sample map;
a i is an i-th fault position;
f(a i , b i ) is a true class label of the i-th fault position;
f(a i , b′) is statistic of a prediction score of each sample image for the i-th fault position when the i-th fault position appears in a plurality of sample images of the fault sample map; and
ρ τ is a temperature scalar.

5 . The method of claim 4 , wherein the statistic is a mean value, a mode or a maximum value.

6 . The method of claim 3 , wherein the position loss comprises:
a first position loss sub-item, used to represent loss between fault prediction boxes of the same fault position in a plurality of sample images in the sample sequence and center points of corresponding detection box labels; a second position loss sub-item, used to represent detection box width loss between the fault prediction boxes of the same fault position in the plurality of sample images and the corresponding detection box labels, and detection box height loss between the fault prediction boxes of the same fault position in the plurality of sample images and the corresponding detection box labels; a third p

Assignees

• Zhejiang Hengyi Petrochemical Co Ltd
• Suqian Yida New Mat Co Ltd

Inventors

• Peng Xiantao
• Wang Peng
• Qiu Yibo
• Liu Mingyi
• Wang Tieming
• Ren Chong
• Li Dake
• Xu Feng

Classifications

• G06T2207/20081

  Training; Learning · CPC title

• G06V10/40

  Extraction of image or video features · CPC title

• G06T2207/30232

  Surveillance · CPC title

• G06T2207/30108

  Industrial image inspection · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title