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This paper proposes a novel framework to automatically pinpoint suspicious sensors that lead to the quality change in physical systems such as manufacture plants. Our framework treats sensor readings as time series, and contains three main stages: time series transformation to feature series, feature ranking, and ranking score fusion. In the first step, we transform time series into a number of different...
We study the problem of identifying discriminative features in Big Data arising from heterogeneous sensors. We highlight the heterogeneity in sensor data from engineering applications and the challenges involved in automatically extracting only the most interesting features from large datasets. We formulate this problem as that of classification of multivariate time series and design shapelet-based...
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