Anomaly detection from aggregate statistics using neural networks

Implementations disclosed describe systems and techniques to detect anomalies in a manufacturing operation. The techniques include generating, using a plurality of outlier detection models, a plurality of outlier scores. The outlier scores are representative of a degree of presence, in a plurality o...

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Bibliographic Details
Main Authors ARMACOST, MICHAEL D, ISKANDAR, JIMMY
Format Patent
LanguageChinese
English
Published 01.04.2024
Subjects
BASIC ELECTRIC ELEMENTS
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR
ELECTRICITY
PHYSICS
SEMICONDUCTOR DEVICES
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Summary:Implementations disclosed describe systems and techniques to detect anomalies in a manufacturing operation. The techniques include generating, using a plurality of outlier detection models, a plurality of outlier scores. The outlier scores are representative of a degree of presence, in a plurality of sensor statistics, of an anomaly associated with the manufacturing operation. Individual outlier scores are generated using a respective one of the plurality of outlier detection models. The techniques further include determining, using the outlier scores, a likelihood of the anomaly associated with the manufacturing operation.
Bibliography:Application Number: TW202110126227