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TIME-SERIES FAULT DETECTION, FAULT CLASSIFICATION, AND TRANSITION ANALYSIS USING A K-NEAREST-NEIGHBOR AND LOGISTIC REGRESSION APPROACH
TIME-SERIES FAULT DETECTION, FAULT CLASSIFICATION, AND TRANSITION ANALYSIS USING A K-NEAREST-NEIGHBOR AND LOGISTIC REGRESSION APPROACH
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机译:使用K近邻和逻辑回归方法的时间序列故障检测,故障分类和过渡分析
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摘要
A method includes receiving historical time-series data and generating training data comprising a plurality of randomized data points associated with the historical time-series data. The historical time-series data was generated by one or more sensors during one or more processes. The method further includes training a logistic regression classifier based on the training data to generate a trained logistic regression classifier. The trained logistic regression classifier is associated with a logistic regression that indicates a location of a transition pattern from a first data point to a second data point. The transition pattern reflects about a reflection point located on the transition pattern. The trained logistic regression classifier is capable of indicating a probability that new time-series data generated during a new execution of the one or more processes matches the historical time-series data.
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