Patent application EP21173505A (EP), filed 2021-05-12
Publication number: EP3913216A1
Title: SYSTEM AND METHOD FOR TRAINING ANOMALY DETECTION ANALYTICS TO AUTOMATICALLY REMOVE OUTLIER DATA
Abstract: A method for detecting anomalies during operation of an asset to improve performance of the asset includes collecting, via a server, data relating to operation of the asset or a group of assets containing the asset. The data includes normal and abnormal asset behavior of the asset or the group of assets containing the asset. Further, the method includes automatically removing, via an iterative algorithm programmed in the server that utilizes one or more inputs or outputs of an anomaly detection analytic, portions of the data containing the abnormal asset behavior to form a dataset containing only the normal asset behavior. The method also includes training, via a computer-based model programmed in the server, the anomaly detection analytic using, at least, the dataset containing only the normal asset behavior. Moreover, the method includes applying, via the server, the anomaly detection analytic to the asset so as to monitor for anomalies during operation thereof.
IPC codes: G06N   7/    00            A I
F03D   7/    04            A I
G06N  20/    00            A I
G06N   7/    01            A I
Claims: number: 1
text: A method for detecting anomalies during operation of an asset to improve performance of the asset, the method comprising:
number: 2
text: The method of claim 1, wherein each asset in the group of assets comprises operational data that follows similar normal data variation patterns when the assets are healthy.
number: 3
text: The method of any of the preceding claims, wherein the plurality of similar assets comprise at least one of a plurality of wind turbines in a wind farm, a plurality of solar panels in a solar farm, a plurality of energy storage systems, or combinations thereof.
number: 4
text: The method of any of the preceding claims, wherein the iterative algorithm comprises a random sample consensus algorithm.
number: 5
text: The method of any of the preceding claims, wherein automatically removing, via the random sample consensus algorithm programmed in the server, the portions of the data containing the abnormal asset behavior to form the dataset containing only the normal asset behavior further comprises:
number: 6
text: The method of any of the preceding claims, wherein the random sample consensus algorithm comprises a group-based random sample consensus algorithm.
number: 7
text: The method of any of the preceding claims, wherein automatically removing, via the group-based random sample consensus algorithm programmed in the server, the portions of the data containing the abnormal asset behavior to form the dataset containing only the normal asset behavior comprises:
number: 8
text: The method of any of the preceding claims, further comprising dividing the data into the multiple subsets based on which assets the data is collected from, whether each subset contains data from one asset or multiple assets, or multiple assets grouped by a distance between assets, installation date, configuration of the assets, service or operating history of the assets, or operating states of the assets.
number: 9
text: The method of any of the preceding claims, wherein the data relating to operation of the asset or the group of assets containing the asset comprises time-series data.
number: 10
text: The method of any of the preceding claims, further comprising training the at least one anomaly detection analytic using the dataset containing only the normal asset behavior and historical data.
number: 11
text: The method of any of the preceding claims, wherein the at least one anomaly detection analytic comprises at least one of a similarity-based modeling (SBM) based anomaly detection analytic.
number: 12
text: The method of any of the preceding claims, wherein the computer-based model comprises at least one of a machine learning algorithm or a statistical model.
number: 13
text: A system for improving performance of at least one asset, the system comprising:
number: 14
text: The system of claim 13, wherein the at least one asset comprises at least one of a wind turbine, a solar panel, an energy storage system, or combinations thereof.
number: 15
text: The system of any of claims 13-14, wherein the iterative algorithm comprises a random sample consensus algorithm.
Office action: search_report, dated 2021-11-24
Rejection reasons: novelty_epc_54
Prior art cited: ref_id: US2019236456A1
ref_type: patent
severity: background
ref_id: - ANONYMOUS: "Random sample consensus - Wikipedia", 30 April 2020 (2020-04-30), XP055849231, Retrieved from the Internet <URL:https://en.wikipedia.org/w/index.php?title=Random_sample_consensus&oldid=954083662> [retrieved on 20211008]
ref_type: npl
severity: background
ref_id: US2019236456
ref_type: patent
severity: novelty_destroying
ref_id: - Anonymous, "Random sample consensus - Wikipedia", (20200430), URL: https://en.wikipedia.org/w/index.php?title=Random_sample_consensus&oldid=954083662, (20211008), XP055849231 [A] 4-7,15 * page  1  - page  3 *
ref_type: npl
severity: background
Final disposition: pending (2021-11-24)