The demonstrator is based on a virtual industrial system (i.e., a production line with multiple machines), which has the common functionalities of high-tech systems, such as lithography machines and professional printers, etc. We have developed a simulator of the system and generate the operational data. As an industrial showcase, ESI’s demonstrator presents “Smart high-tech system-diagnostics with operational data”.
One of the fundamental issues of root-cause analysis is how to detect anomalies from data. Yazzoom’s advanced unsupervised anomaly detection techniques have been applied by ESI’s demonstrator in the following two aspects.
To diagnose a specific machine issue and narrow down the space of causes, we use the operational data generated by the machine components. Basically, anomalies are identified based on the operational data. This is achieved using unsupervised learning techniques, which are able to detect anomalies without efforts from domain experts.
Read more.
One of the fundamental issues of root-cause analysis is how to detect anomalies from data. Yazzoom’s advanced unsupervised anomaly detection techniques have been applied by ESI’s demonstrator in the following two aspects.
To diagnose a specific machine issue and narrow down the space of causes, we use the operational data generated by the machine components. Basically, anomalies are identified based on the operational data. This is achieved using unsupervised learning techniques, which are able to detect anomalies without efforts from domain experts.
Read more.
