Streaming Synthetic Time Series for Simulated Condition Monitoring
J. Zenisek, J. Wolfartsberger, C. Sievi, M. Affenzeller - Streaming Synthetic Time Series for Simulated Condition Monitoring - IFAC Symposium on Information Control Problems in Manufacturing, Bergamo, Italy, 2018, pp. 643-648
The transformation of the common production plant to a cyber-physical system is one of the most recent developments in manufacturing industry. The analysis of data streams produced by such sensor-equipped plants promises potential for process optimization. However, for competitive reasons and due to the novelty of the development, the access to publicly available real-world data is quite limited. Hence, businesses and researchers new to this area are often confronted with the cumbersome and complex task of synthesizing time series in order to gain ﬁrst experiences. This work presents a novel concept and a corresponding software implementation for the stream-wise generation and publication of sensor data, to simulate condition monitoring of industrial production plants. The resulting conﬁguration ﬁle-driven tool is capable of acting like a sensor-equipped plant by generating data points from various Gaussian Process-based models and mathematical expressions, or by replaying data sets. Moreover, it can be seamlessly connected with existing surrounding systems by using the MQTT protocol. In this context, the software aims at laying the foundation for real-world applications and improving them by providing a simulation tool to prototype and test with.