Complex machines such as laser systems are controlled by a huge set of parameters and a multitude of sensors. Their temporal behavior can be analyzed to gain insights in their operating status, meantime-between-failure (MTB) and to prevent malfunction. Finding cross-correlations and connections can be essential for further optimization. Furthermore, a long-term dataset can help to identify faulty behavior and, thus, significantly simplify a repair.
We have developed a SQL-based architecture to store these huge datasets based on our control software and sensor data. My interactive analysis tool based on Python, Qt and pyqtgraph can visualize the recorded dataset independently on the users operating system (Windows, Linux and macOS).
Such an advanced and interactive analysis tool is a necessity for huge datasets, otherwise the stored data is a useless set of bits and bytes.