Anomaly detection using a pre-trained global model

Patent metadata

Field	Value
Publication number	US-2024362461-A1
Application number	US-202418644880-A
Country	US
Kind code	A1
Filing date	Apr 24, 2024
Priority date	Apr 28, 2023
Publication date	Oct 31, 2024
Grant date	—

Abstract

A method of behavior monitoring includes receiving, at a device, sensor data from one or more sensors associated with a monitored asset. The method also includes applying, at the device, a data scaling operation to input data to generate scaled input data for a pre-trained global model. The input data is based on the sensor data. The method further includes providing, at the device, the scaled input data to the pre-trained global model to selectively generate an alert.

First claim

What is claimed is:

1 . A method of behavior monitoring, the method comprising:
receiving, at a device, sensor data from one or more sensors associated with a monitored asset; applying, at the device, a data scaling operation to input data to generate scaled input data for a pre-trained global model, the input data based on the sensor data; and providing, at the device, the scaled input data to the pre-trained global model to selectively generate an alert.

2 . The method of claim 1 , further comprising:
receiving historical sensor data indicative of operation of the monitored asset; and determining the data scaling operation that, when applied to historical input data, generates scaled historical data having target statistical characteristics associated with the pre-trained global model, wherein the historical input data is based on the historical sensor data.

3 . The method of claim 2 , wherein the target statistical characteristics include at least one of a target maximum value, a target minimum value, a target distribution, a target average value, a target standard deviation, a target interquartile range (IQR), a target linear scaling, or a target non-linear scaling.

4 . The method of claim 2 , further comprising selecting a first subset of the historical sensor data that is indicative of normal operation of the monitored asset, wherein the historical input data is based on the first subset of the historical sensor data.

5 . The method of claim 2 , further comprising:
selecting a second subset of the historical sensor data; applying the data scaling operation to validation data to generate scaled validation data for the pre-trained global model, the validation data based on the second subset of the historical sensor data; providing the scaled validation data to the pre-trained global model to generate reconstructed validation data; and validating the pre-trained global model based on a comparison of the scaled validation data and the reconstructed validation data.

6 . The method of claim 2 , further comprising:
obtaining historical sensor data sets associated with multiple assets; and applying data scaling operations to training data sets to generate scaled training data sets, each scaled training data set generated to have the target statistical characteristics, wherein a particular training data set is based on a particular historical sensor data set associated with a particular asset of the multiple assets, wherein the pre-trained global model is generated based on the scaled training data sets.

7 . The method of claim 1 , wherein the monitored asset includes a mechanical device, an electromechanical device, an electrical device, an electronic device, or a combination thereof.

8 . The method of claim 1 , further comprising:
providing the scaled input data to an anomaly detection model to generate an anomaly score; and providing the anomaly score to an alert generation model to selectively generate the alert.

9 . The method of claim 8 , wherein the anomaly detection model includes an autoencoder.

10 . The method of claim 1 , further comprising:
using the pre-trained global model to process the scaled input data to generate an anomaly score; and performing a sequential probability ratio test on the anomaly score to determine whether to generate the alert.

11 . A system for behavior monitoring, the system comprising:
one or more processors configured to:
receive sensor data from one or more sensors associated with a monitored asset;
apply a data scaling operation to input data to generate scaled input data for a pre-trained global model, the input data based on the sensor data; and
provide the scaled input data to the pre-trained global model to selectively generate an alert.

12 . The system of claim 11 , wherein the one or more processors are further configured to:
receive historical sensor data indicative of operation of the monitored asset; and determine the data scaling operation that, when applied to historical input data, generates scaled historical data having target statistical characteristics associated with the pre-trained global model, wherein the historical input data is based on the historical sensor data.

13 . The system of claim 12 , wherein the one or more processors are further configured to select a first subset of the historical sensor data that is indicative of normal operation of the monitored asset, wherein the historical input data is based on the first subset of the historical sensor data.

14 . The system of claim 12 , wherein the one or more processors are further configured to:
select a second subset of the historical sensor data; apply the data scaling operation to validation data to generate scaled validation data for the pre-trained global model, the validation data based on the second subset of the historical sensor data; provide the scaled validation data to the pre-trained global model to generate reconstructed validation data; and validate the pre-trained global model based on a comparison of the scaled validation data and the reconstructed validation data.

15 . The system of claim 12 , wherein the one or more processors are further configured to:
obtain historical sensor data sets associated with multiple assets; and apply data scaling operations to training data sets to generate scaled training data sets, each scaled training data set generated to have the target statistical characteristics, wherein a particular training data set is based on a particular historical sensor data set associated with a particular asset of the multiple assets, wherein the pre-trained global model is generated based on the scaled training data sets.

16 . The system of claim 11 , wherein the monitored asset includes a mechanical device, an electromechanical device, an electrical device, an electronic device, or a combination thereof.

17 . The system of claim 11 , wherein the pre-trained global model includes an anomaly detection model and an alert generation model, and wherein the one or more processors are configured to:
provide the scaled input data to the anomaly detection model to generate an anomaly score; and provide the anomaly score to the alert generation model to selectively generate the alert.

18 . The system of claim 11 , wherein the one or more processors are configured to:
use the pre-trained global model to process the scaled input data to generate an anomaly score; and perform a sequential probability ratio test on the anomaly score to determine whether to generate the alert.

19 . A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to:
receive sensor data from one or more sensors associated with a monitored asset; apply a data scaling operation to input data to generate scaled input data for a pre-trained global model, the input data based on the sensor data; and provide the scaled input data to the pre-trained global model to selectively generate an alert.

20 . The non-transitory computer-readable storage medium of claim 19 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
receive historical sensor data indicative of operation of the monitored asset; and determine the data scaling operation that, when applied to historical input data, generates scaled historical data having target statistical characteristics associated with

Assignees

• Sparkcognition Inc

Inventors

• Jain Akhilesh
• Morganthal Allyson

Classifications

• G06N3/088

  Non-supervised learning, e.g. competitive learning · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/0455 Primary

  Auto-encoder networks; Encoder-decoder networks · CPC title