Systems and methods for anomaly detection of physical assets

Patent metadata

Field	Value
Publication number	US-2024412617-A1
Application number	US-202418736896-A
Country	US
Kind code	A1
Filing date	Jun 7, 2024
Priority date	Jun 9, 2023
Publication date	Dec 12, 2024
Grant date	—

Abstract

In accordance with the present disclosure, anomaly detection systems and methods are disclosed for automatically detecting physical anomalies using image data. The anomaly detection systems and methods disclosed herein can be used to detect anomalies in physical assets, such as anomalies present within a building, at a site, and/or anomalies associated with a piece of equipment. Image data is received from one or more cameras that are configured to capture image data of a physical asset. A probability of a physical anomaly being present in the image data is determined using an artificial intelligence model. The image data may be further analyzed to determine that the physical anomaly is a specific type of anomaly. An alert is output when the probability of the physical anomaly being present in the image data exceeds a threshold. A method of training an anomaly detection model is also disclosed.

First claim

1 . An anomaly detection method, comprising:
receiving image data from one or more cameras configured to capture an image of a physical asset; determining a probability of a physical anomaly being present in the image data using an artificial intelligence model that is trained to detect anomalous image data; and outputting an alert when the probability of the physical anomaly being present in the image data exceeds a threshold value.

2 . The anomaly detection method of claim 1 , further comprising:
analyzing the image data to determine that the physical anomaly is a specific type of anomaly; and outputting the alert including information on the specific type of anomaly present in the image data.

3 . The anomaly detection method of claim 2 , wherein analyzing the image data to determine that the physical anomaly is the specific type of anomaly comprises determining a probability that the physical anomaly is the specific type of anomaly using one or more secondary artificial intelligence models that are trained to predict the physical anomaly as being one or more types of anomalies.

4 . The anomaly detection method of claim 3 , wherein the one or more secondary artificial intelligence models comprise a model that is trained to perform image segmentation on the image data to classify the anomaly as being the specific type of anomaly.

5 . The anomaly detection method of claim 3 , wherein the one or more secondary artificial intelligence models comprise a multi-modal generative AI model.

6 . The anomaly detection method of claim 5 , further comprising receiving a context of the image data, wherein the multi-modal generative AI model uses the context of the image data to determine the probability that the physical anomaly is the specific type of anomaly.

7 . The anomaly detection method of claim 5 , further comprising receiving audio data and/or vibration data associated with the physical asset, wherein the multi-modal generative AI model uses the received audio and/or vibration data to determine the probability that the physical anomaly is the specific type of anomaly.

8 . The anomaly detection method of claim 5 , wherein the multi-modal generative AI model is configured to generate an output comprising one or both of: a description of the specific type of anomaly, and a suggested action for responding to the specific type of anomaly.

9 . The anomaly detection method of claim 2 , wherein determining that the physical anomaly is the specific type of anomaly comprises applying one or more rules to the image data.

10 . The anomaly detection method of claim 2 , further comprising receiving auxiliary data associated with the physical asset from one or more sensors, and wherein determining that the physical anomaly is the specific type of anomaly is further based on the auxiliary data.

11 . The anomaly detection method of claim 2 , wherein analyzing the image data to determine that the physical anomaly is the specific type of anomaly is performed automatically when the probability that the physical anomaly is present in the image data exceeds the threshold value.

12 . The anomaly detection method of claim 2 , wherein analyzing the image data to determine that the physical anomaly is a specific type of anomaly is performed in response to a prompt to identify the specific type of anomaly.

13 . The anomaly detection method of claim 2 , further comprising receiving user feedback on the specific type of anomaly, and updating the one or more secondary artificial intelligence models based on the user feedback.

14 . The anomaly detection method of claim 1 , further comprising, in response to outputting the alert, receiving user feedback that the image data is normal, and updating the artificial intelligence model based on the user feedback.

15 . An anomaly detection system, comprising:
a processor; and a non-transitory computer-readable memory having stored thereon computer-executable instructions which, when executed by the processor, configure the anomaly detection system to perform the anomaly detection method of claim 1 .

16 . A method of training an anomaly detection model, comprising:
obtaining training images comprising normal image data; and training an artificial intelligence model to determine a probability of a physical anomaly being present in the image data.

17 . The method of claim 16 , further comprising:
obtaining training images comprising anomalous image data that have a known anomaly; and training one or more secondary artificial intelligence models to determine the known anomaly present in the anomalous image data.

18 . The method of claim 17 , wherein the one or more second artificial intelligence models comprise a multi-modal generative AI model, and the method further comprises:
obtaining additional training data comprising an additional input type; and training the multi-modal generative AI model to determine the known anomaly present in the anomalous image data based on the training images comprising anomalous image data and the additional training data.

19 . The method of claim 18 , further comprising training the multi-modal generative AI model to generate outputs associated with the known anomaly in response to different input prompts.

20 . An anomaly detection model trained in accordance with the method of claim 16 .

Assignees

• Bce Inc

Inventors

• Legare Francois
• Vanier Francis
• Abbas Jafar
• Ménard-Lamarre Nordés

Classifications

• G06T2207/20081

  Training; Learning · CPC title

• G06T7/11

  Region-based segmentation · CPC title

• G06T2207/30164

  Workpiece; Machine component · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06T7/0004 Primary

  Industrial image inspection · CPC title