A Quality Control Engine for Complex Physical Systems

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 feature series to describe the underlying dynamics of each sensor data. After that, the importance scores of all feature series are computed by utilizing several feature selection and ranking techniques, each of which discovers specific aspects of feature importance and their dependencies in the feature space. Finally we combine importance scores from all the rankers and all the features to obtain the final ranking of each sensor with respect to the system quality change. Our experiments based on synthetic time series as well as sensor data from a real system demonstrate the effectiveness of proposed method. In addition, we have implemented our framework as a production engine, and successfully applied it to several real physical systems.