Background
Today, we are at the beginning of Industry 4.0. Machines are becoming increasingly sensorized and connected to the internet. Streaming data will thus be sent continuously to cloud computing data-centers. Condition monitoring techniques can leverage these huge volumes of available data to increase detection potential and insights in system behavior by long-term trending, anomaly detection and learning approaches.
Additionally, the fact that data of similar machines of a fleet is collected allows for exploiting system similarity. At our research group we use an integrated monitoring approach for the Industry 4.0 context. Some of the topics included in this approach are:
- Cloud computing
- Failure detection and severity assessment
- Asset performance tracking
- Machine Learning
Measured data can not only serve as a monitoring tool but also a means of validating the design of the equipped machine. One of our strongest tools for this purpose is the use of modal analysis. Gaining insights into the modal behavior of a machine allows for pinpointing flaws or opportunities in the design.
