Early identification and economic assessment of process anomalies

02.06.2021 Siemens has announced its AI Anomaly Assistant Industrial App, which uses artificial intelligence (AI) to detect anomalies in the process industry and assess their business relevance.

Artificial intelligence gives companies new opportunities to optimise their processes economically.
© Photo: Siemens
Artificial intelligence gives companies new opportunities to optimise their processes economically.

This gives companies new opportunities for the economical optimisation of their processes. The app analyses process events that affect parameters such as productivity, availability, and quality, and alerts the plant operator to any anomalies. These events and anomalies are no longer simply identified, but also scrutinised for their business relevance—an assessment which was previously only possible based on previous experience.

Machine learning with process data

To enable the AI to detect and evaluate business-relevant anomalies, the machine-learning algorithms are trained on the basis of process data and then concentrated to determine which anomalies have an impact on the economic efficiency of the plant. The plant operators themselves then define the further focus of the AI using the app dashboard, where anomalies can be selected, evaluated and commented. This evaluation phase is accompanied by several feedback loops, so that the plant operator ends up with well-trained, focused AI that is able to evaluate anomalies, based on the process data, for their business relevance.

Cloud application or installed within the user’s own infrastructure

The AI Anomaly Assistant app is installed either as a cloud application or within the user's own infrastructure, for example on a Simatic Box PC or a virtual machine. The cloud-based solution is particularly advantageous during the training and evaluation phase, since it supports efficient collaboration between data analysts and plant operators. In addition, it also allows the results of anomaly detection to be combined with other services, such as predictive asset management, as part of the Asset Performance Suite (APS).