Intelligent Control & Automation

Achieve optimized performance through the Sentian ICA

Industrial AI is not only about predictive maintenance. It is also supposed to make your machines and plants run closer to optimum. It is often found that there are substantial performance differences between different teams and machine operators - with varying levels of waste and output quality.

Using advanced algorithms with data from control systems, automation systems, machines, ambient sensors, KPI’s and operators, the Sentian ICA can improve machine and plant performance.

Our solution is an advanced deep learning and multi-AI solution for pattern recognition, predictions and optimization.

Using the latest in AI we find the formulas, actions and expertise to improve your operations. It is also fast to implement, learns quickly and offers fast ROI. Additionally, it can be implemented on single machines, plant wide or cross-plants.

• Single or fleet of machines

• Integrate to existing systems

• Cloud and Edge

• Increase performance by waste reduction

• Improve quality and increase production

• Improved control of operations

Capturing Manufacturing Expertise

Experienced machine operators and production managers often have considerably better output volumes, higher quality and less downtime than inexperienced ones. Our Sentian ICA improves performance for all operators.

Control System Optimization

Commonly used complex models have often been created when less data was available and without using modern AI. By the use of machine learning and more data, these models can often be optimized. This is what our Sentian ICA does.

Features

• Handles multiple types of data such as machine data, weather, new sensor data and other sources

• Automatic learning

• Localized high performance solution for real-time response and cloud integration

• Generalization of models for multi site implementation

Control and Automation systems

Industry 4.0 and industrial AI has for a long time been focused on predictive maintenance and anomaly detection. However that only makes machines more available. The next step is to make them operate closer to optimum. There are several aspects to this. At the core control and automation systems have a fundamental limitation of being programmed by people. Machine Learning can in most cases improve the models and target values, that are used in production today. Another level is people. Expert machine operators can have a much higher performance than others operators.

The same logic goes for full shifts of larger production sites where some teams perform much above the others. Using modern AI we can improve the performance of the machines and plants. This may also be key for corporations with an aging workforce that find it hard to replace the employees that are retiring.

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