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METHOD AND SYSTEM FOR SEMI-SUPERVISED DEEP ANOMALY DETECTION FOR LARGE-SCALE INDUSTRIAL MONITORING SYSTEMS BASED ON TIME-SERIES DATA UTILIZING DIGITAL TWIN SIMULATION DATA
METHOD AND SYSTEM FOR SEMI-SUPERVISED DEEP ANOMALY DETECTION FOR LARGE-SCALE INDUSTRIAL MONITORING SYSTEMS BASED ON TIME-SERIES DATA UTILIZING DIGITAL TWIN SIMULATION DATA
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机译:基于时间序列数据利用数字双模拟数据的半导体工业监测系统的半监控深度异常检测方法和系统
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摘要
A computer-implemented method for detecting an anomalous operating status of a technical system. A training phase obtains a first set of time-series values generated by a digital twin simulation of the technical system for a regular operating status and a second set of time-series values measured by sensors in an anomalous operating status, and adjusts parameters of a machine learning model for detecting the regular operating status and for discriminating data samples of the regular operating status from data samples of the anomalous operating status to generate a trained machine learning model. A monitoring phase obtains a set of multivariate time-series values measured by the sensors, calculates an anomaly score value for determining whether the technical system is in an anomalous operating status based on the obtained set of multi-variate time-series values and the trained machine learning model, and outputs a signal including information on the determined anomalous operating status.
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