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Early identification and economic assessment of process anomalies with AI-based app

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.

Early identification and economic assessment of process anomalies with AI-based app
Artificial intelligence gives companies new opportunities to optimize their processes economically.

The app analyzes 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 scrutinized for their business relevance—an assessment which was previously only possible based on previous experience.

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 operator themself then defines 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.


Early identification and economic assessment of process anomalies with AI-based app
Siemens presents the AI Anomaly Assistant App, which uses artificial intelligence (AI) to detect anomalies in the process industry and assess their business relevance. A dashboard helps users classify and evaluate events.

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).

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