Enhancing the control of industrial processes and the quality of products can be supported by learning from sensor data. Project B3 focuses on the investigation of how decentralised data mining can be used for real-time quality predictions and how it can be integrated into production processes.
The tasks of data analytics in production systems have been ordered according to their difficulty: (1) anomaly detection, (2) diagnostic analytics, (3) predictive analytics, and (4) prescriptive analytics. In the first phase, we succeeded in developing a new decentralised anomaly detection and invented learning from label proportions. In the second phase, we investigated diagnostic and predictive analytics. Diagnostic analytics aims at explaining not ok products by process data. Based on time series data from a hot rolling process for steel-bars production, features have been extracted and aggregated that help to distinguish between ok and not ok products. Modeling the overall preprocessing in the software RapidMiner took the toolbox to its extreme, becoming a high-level programming environment. The advantage is the reproducibility of RapidMiner processes and their understandable documentation. Predictive analytics in flexible production processes has contributed to adequate process control in real-time. For distributed data mining, the training of local models from counts (TLMC) for vertically partitioned data has been developed. The anomaly detection has been enhanced for distributed settings as they are given by the Internet of Things. In the third phase, prescriptive analytics will be the focus of the project; i.e., the learned model changes the process in real-time. An example is not only to recognise a deviation of sensor measurements from the normal, but also to adapt the process parameters accordingly.
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