Abstract
Nowadays big data is widely adopted in industry field. In an advanced manufacturing system hundreds of sensors are deployed to collect key variables for system performance and the real-time data would be used for further monitoring and anomaly detection. However, there are many challenges for applying the sensor-based data directly, including the profile data has unsynchronized different length for different samples, the existence of obvious long-term drift, strong correlation of sensor clusters and the particular feature extraction. To solve these problems this invention presents a multiple profiles sensor-based engineering data processing system, including (1) preprocessing the signals to align the data and remove long-term drift, (2) clustering the sensors which have strong correlations, and (3) extracting particular features from different sensor clusters.
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