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DETERMINING CURRENT AND FUTURE STATES OF INDUSTRIAL MACHINES BY USING A PREDICTION MODEL BASED ON HISTORICAL DATA
DETERMINING CURRENT AND FUTURE STATES OF INDUSTRIAL MACHINES BY USING A PREDICTION MODEL BASED ON HISTORICAL DATA
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机译:基于历史数据的预测模型确定工业机械的电流和未来状态
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摘要
A first industrial machine (110) provides historical event data (130) to a model generation computer (101) that generates a prediction model. A behavior prediction computer (102) uses the model (150) in combination with past and current data (140) from a second machine (120) - the industrial machine under supervision (IMUS) - and provides feedback to the IMUS. Both machines (110, 120) have common properties. Generating the prediction model comprises to obtain event features and event vectors, to cluster the vectors and to assign clusters to machine states, and to identify probabilities of machine state transitions. Features, vectors and clusters are processed by processing techniques, with some of the techniques are natural language processing techniques (NLP).
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