Streaming Synthetic Time Series for Simulated Condition Monitoring

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)643-648
Number of pages6
JournalIFAC-PapersOnLine
Volume51
Issue number11
DOIs
Publication statusPublished - 1 Jan 2018
Event16th IFAC Symposium on Information Control Problems in Manufacturing - Bergamo, Italy
Duration: 11 Jun 201813 Jun 2018
http://www.incom2018.org/

Keywords

  • Condition Monitoring
  • Industry Automation
  • Predictive Maintenance
  • Simulation-based Decision Making
  • Time Series Generation

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