The high complexity of manufacturing processes and the continuously growing

amount of data lead to excessive demands on the users with respect to process monitoring, data analysis and fault detection. For these reasons, problems and faults are often detected too late, maintenance intervals are chosen too short and optimization potential for higher output and

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increased energy eciency is not suciently used. A possibility to cope with these challenges

is the development of self-learning assistance systems, which identify relevant relationships

by observation of complex manufacturing processes so that failures, anomalies and need for

optimization are automatically detected. The assistance system developed in the present work

accomplishes data acquisition, process monitoring and anomaly detection in industrial and

agricultural processes. The assistance system is evaluated in three application cases: Large

distillation columns, agricultural harvesting processes and large-scale sorting plants. In this

paper, the developed infrastructures for data acquisition in these application cases are described as well as the developed algorithms and initial evaluation results.

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With the advances in new-generation information technologies, especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. Intelligence comes from data. Integrated analysis for the manufacturing big data is benecial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance. On this basis, the similarities and differences between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twin can be complementary, how they can be integrated to promote smart manufacturing are discussed.